Abstract DDoS attacks are as perilous as it was ten years before

Abstract
DDoS attacks are as perilous as it was ten years before. This attack also getting more advanced with advancement of network technology from the simple network architecture of cloud and Fog architecture. That is why counter measure deployed for such attacks should act in a timely manner and should be positioned as near as possible to the Botnet to prevent DDoS. The situation was not that complex as it is getting now with the new technology known as IoT. Imagine if 25 billion of IoT Devices generate big data that can be used for DDoS attack what will be the consequence. That’s is why we try to mitigate DDoS attack at very initial level with the help of Fog Architecture. This open more challenge and issue regarding securing the network’s hardware and software configuration its self from the attack. So we propose a new complete framework With Cloud, Fog, SDN, NFV and Blockchain technology to minimize the aftermath of DDoS attack. To combine the properties of these techniques and technology, a new framework and algorithms are the need of time. Our framework is designed to localize DDoS attack at the initial level, preventing waste of resources while defending its precious assets. This paper also provides opportunities for different could or companies to secure their assets by utilizing power of Blockchain and smart contracts.

Introduction
We are not only in the age of technologyy,butt also observing decent advancement that is making our lives better. This is due to the rapid development in technology and downfall of prices. Now internet is one of the basic need of life from domestic to business usersADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “ISSN” : “09486968”, “abstract” : “The last years we faced a tremendous development of social media powered by innovative technologies related to web services, web 2.0 and social networks. Nowadays we are entering in to the next digitally enriched generation of social media and enabled by thrilling, currently under extensive development technologies, like Big Data, Cloud Computing, Virtual/Augmented Reality and Internet of Things. To our understanding the new generation of social media and the integrated business models that will support them are within a converging area where social features and ubiquitous technologies are met. Social elements, related to behavior, self-esteem, attachment and other psychological dimensions of personality will be transparently integrated to a number of technologies. Thus for the next years we have to wait for a number of new applications and systems, all targeted in a meta-existence level where the characteristics of human identity will be mixed with various digital identity elements. This basic trend and direction in the Social Media research in the next decade will boost the transparency of technologies and will stimulate an extremely different Web than the one we are exploiting together. The key dimension of the Social Media sphere will be the dependent social identification of humans by a number of technologies.”, “author” : { “dropping-particle” : “”, “family” : “Lytras”, “given” : “Miltiadis D.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Al-Halabi”, “given” : “Wadee”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Zhang”, “given” : “Jacky Xi”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Haraty”, “given” : “Ramzi A.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Masud”, “given” : “Mehedi”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Universal Computer Science”, “id” : “ITEM-1”, “issue” : “11”, “issued” : { “date-parts” : “2015” }, “page” : “1379-1384”, “title” : “Enabling technologies and business infrastructures for next generation social media: Big data, cloud computing, internet of things and virtual reality”, “type” : “article-journal”, “volume” : “21” }, “uris” : “http://www.mendeley.com/documents/?uuid=324dc42a-4992-49d1-8ab0-cee5f178b175” } , “mendeley” : { “formattedCitation” : “1”, “plainTextFormattedCitation” : “1”, “previouslyFormattedCitation” : “1” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }1. Nowadays, every office, home is connected to the internet with a broadband connection or other types of communication medium. Internet even helping traveler to find their waysADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/J.TRC.2018.03.009”, “ISSN” : “0968-090X”, “abstract” : “New mobility data sources like mobile phone traces have been shown to reveal individualsu2019 movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including oneu2019s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a travelleru2019s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the u201ctourismu201d category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.”, “author” : { “dropping-particle” : “”, “family” : “Wang”, “given” : “Yihong”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Correia”, “given” : “Gonu00e7alo Homem de Almeida”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Arem”, “given” : “Bart”, “non-dropping-particle” : “van”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Timmermans”, “given” : “H.J.P. 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But the traditional networking principles are not that much changed, they are built using the switches and routers which are still mainly being configured manually by the operators. So, it arises some of the risks to network devices, interfaces and hosts, vulnerabilities arises due to obsolete patches in network devices. There are also chances of attacks on admin servers due to lack of security and also the single point of failure due to unreliable machines.

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Updated network devices now support high transmission rate that are available to general publicADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/COMST.2016.2532458”, “ISBN” : “1553-877X”, “ISSN” : “1553-877X”, “abstract” : “The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity and significant improvement in usersu2019 perceived Quality of Service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency and QoS are expected to be the panacea of most of the current cellular networksu2019 problems. In this paper, we make an exhaustive review of wireless evolution towards 5G networks. We first discuss the new architectural changes associated with the Radio Access Network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes, needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests and simulation experiments. 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Researchers all over the world finding open challenges and making future communication even more reliable and fasterADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “abstract” : “Customary network migration to Next Generation Networks (NGN) is due to service integration and low cost of the offered services in NGN. NGN is based on an IP/MPLS backbone through which all traffics pass. Besides, different mobile generations up to R99 UMTS are all based on the circuit switch systems. Thus the connections to NGN may cause some challenges. 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With the fast increasing requirements of Mobile Internet services, the Internet and Mobile Networks go to the convergence. Mobile Networks can also get benefits from the SDN evolution to fulfill the 5th Generation (5G) capacity booming. The article implements SDN into Frameless Network Architecture (FNA) for 5G Mobile Network evolution with proposed Mobile-oriented OpenFlow Protocol (MOFP). The Control Plane/User Plane (CP/UP) separation and adaptation strategy is proposed to support the User-Centric scenario in FNA. The traditional Base Station is separated with Central Processing Entity (CPE) and Antenna Element (AE) to perform the OpenFlow and Network Virtualization. The AEs are released as new resources for serving users. 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To meet these demands next-generation mobile network operators will deploy small cells next to conventional base station structure intensively to enhance capacity and service coverage for customers. Implementation of seamless handover between first tier (macrocell layer) and second tier (small cells) is one of the key challenges to fulfill the QoS requirements. In this paper we introduce the reader to the details of the handover procedure: information gathering, decision strategies and the base station exchange process. All these three phases face difficulties in multi-tier networks. High dense femtocell deployment makes handover related information gathering very hard, efficient handover decision mechanisms are vital to reduce the number of unnecessary handovers and avoid ping-pong effect. Last but not least base station exchange has to be accelerated to achieve appropriate system performance. 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Within 3GPP, Service and System Aspects Working Group 2 (SA2) is responsible for identifying the main functions and entities of the network. In December 2016, the 3GPP SA2 group finalized the first phase of study for the architecture and main functions of 5G mobile communication system under the study item of Next Generation system (NextGen). Currently, normative standardization is on-going based on the agreements made in the NextGen Phase 1 study. 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But more transmission rate means more network attacks and high in volume taking less time to reach attack destination.

The word volume does not come alone. We often hear words like varsity and velocity. These words point to a new era of big Data science. This create a more questionable scenario for security. Now security and privacy is on the stack again and with high transmission rate and with big amount of data. Big data analysis require more computational power and storage capacity that cannot be done at the edge. Only solution is to do this analysis part at the cloud which is very costly as far as delay matters, as data have to reach cloud server, thus consuming bandwidth of the network. But as the only option researcher are working to analysis big data on cloud servers to mitigate attacks like DDoS attacksADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1145/3154273.3154346”, “ISBN” : “9781450363723”, “author” : { “dropping-particle” : “”, “family” : “Neupane”, “given” : “R L”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Neely”, “given” : “T”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Chettri”, “given” : “N”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Vassell”, “given” : “M”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Zhang”, “given” : “Y”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Calyam”, “given” : “P”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Durairajan”, “given” : “R”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “ACM International Conference Proceeding Series”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2018” }, “title” : “Dolus: Cyber defense using pretense against DDoS attacks in cloud platforms”, “type” : “article-journal”, “volume” : “Part F1331” }, “uris” : “http://www.mendeley.com/documents/?uuid=50fbc737-d210-4f39-bc51-9216fb40e9e5” }, { “id” : “ITEM-2”, “itemData” : { “DOI” : “10.1016/j.comcom.2017.03.010”, “ISBN” : “1047-6210”, “ISSN” : “01403664”, “PMID” : “11242594”, “abstract” : “Security issues related to the cloud computing are relevant to various stakeholders for an informed cloud adoption decision. Apart from data breaches, the cyber security research community is revisiting the attack space for cloud-specific solutions as these issues affect budget, resource management, and service quality. Distributed Denial of Service (DDoS) attack is one such serious attack in the cloud space. In this paper, we present developments related to DDoS attack mitigation solutions in the cloud. In particular, we present a comprehensive survey with a detailed insight into the characterization, prevention, detection, and mitigation mechanisms of these attacks. Additionally, we present a comprehensive solution taxonomy to classify DDoS attack solutions. We also provide a comprehensive discussion on important metrics to evaluate various solutions. This survey concludes that there is a strong requirement of solutions, which are designed keeping utility computing models in mind. Accurate auto-scaling decisions, multi-layer mitigation, and defense using profound resources in the cloud, are some of the key requirements of the desired solutions. In the end, we provide a definite guideline on effective solution building and detailed solution requirements to help the cyber security research community in designing defense mechanisms. To the best of our knowledge, this work is a novel attempt to identify the need of DDoS mitigation solutions involving multi-level information flow and effective resource management during the attack.”, “author” : { “dropping-particle” : “”, “family” : “Somani”, “given” : “Gaurav”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Gaur”, “given” : “Manoj Singh”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Sanghi”, “given” : “Dheeraj”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Conti”, “given” : “Mauro”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Buyya”, “given” : “Rajkumar”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Computer Communications”, “id” : “ITEM-2”, “issued” : { “date-parts” : “2017” }, “page” : “30-48”, “publisher” : “Elsevier B.V.”, “title” : “DDoS attacks in cloud computing: Issues, taxonomy, and future directions”, “type” : “article-journal”, “volume” : “107” }, “uris” : “http://www.mendeley.com/documents/?uuid=71a3fbee-025d-43b3-a03b-548395bd809d” }, { “id” : “ITEM-3”, “itemData” : { “DOI” : “10.1016/j.jnca.2016.01.001”, “ISBN” : “9781467392990”, “ISSN” : “10958592”, “PMID” : “25218122”, “abstract” : “Despite the increasing popularity of cloud services, ensuring the security and availability of data, resources and services remains an ongoing research challenge. Distributed denial of service (DDoS) attacks are not a new threat, but remain a major security challenge and are a topic of ongoing research interest. Mitigating DDoS attack in cloud presents a new dimension to solutions proffered in traditional computing due to its architecture and features. This paper reviews 96 publications on DDoS attack and defense approaches in cloud computing published between January 2009 and December 2015, and discusses existing research trends. A taxonomy and a conceptual cloud DDoS mitigation framework based on change point detection are presented. Future research directions are also outlined.”, “author” : { “dropping-particle” : “”, “family” : “Osanaiye”, “given” : “Opeyemi”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Choo”, “given” : “Kim Kwang Raymond”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Dlodlo”, “given” : “Mqhele”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Journal of Network and Computer Applications”, “id” : “ITEM-3”, “issued” : { “date-parts” : “2016” }, “page” : “147-165”, “title” : “Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework”, “type” : “article”, “volume” : “67” }, “uris” : “http://www.mendeley.com/documents/?uuid=10efd03a-ca28-4c20-86a0-67ad0cb25d77” } , “mendeley” : { “formattedCitation” : “8u201310”, “plainTextFormattedCitation” : “8u201310”, “previouslyFormattedCitation” : “8u201310” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }8–10.
IoT is another big leap in the technology to make life better and easier. From fire sensor in the forest and in homes using CNN and other modelsADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.3390/jsan7010011”, “ISSN” : “2224-2708”, “abstract” : “u00a9 2018 by the authors. Fires usually occur in homes because of carelessness and changes in environmental conditions. They cause threats to the residential community and may result in human death and property damage. Consequently, house fires must be detected early to prevent these types of threats. The immediate notification of a fire is the most critical issue in domestic fire detection systems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a consequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and low-power sensor devices that have the ability to sense the environment and can provide real-time fire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor network using multiple sensors for early detection of house fires. In addition, we used the Global System for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire detection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a language program. 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IoT is getting more mature as many researcher interested in the field. As a result IoT is evolving at very high rate of speed. 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But this also making concern for data scientist.

But researcher and hacker also contributing to maintain security and privacy. Recent advancement is known as Blockchain.
Contribution
Author provide a complete framework to mitigate possible DDoS in Fog and provide algorithm that helps Fog network to reroute data to firewall or cloud as required. As fog will work along with Blockchain, author also introduce an algorithm to add new node in fog Blockchain or to revoke the smart contract as needed. As expansion property of Blockchain author also provide an algorithm that can help organization to expand their Blockchain to enhance security features. This algorithm has two versions according to two different scenarios according to need of organization. Overall author provides a comprehensive framework to mitigate DDoS attack and provide security for software and hardware assets to the company in detail.

Organization
Motivation
Distributed Denial of Service (DDoS)
Distributed denial of service attack is popular among hackers. It can vary from very simple to complex depending on the nature of the DDoS attack. Its requirement seems to be very simple as only three basic components are required.

Attacker
Botnets
Victim
The attacker does not attack directly. It just made some modification on another computer that is not properly secured. These devices could vary from handheld devices to proper servers.

In this paper, the Denial of Service attacks will be divided into three types taken in the consideration their nature and effects in the cloud computing environment. This includes:
The only and foremost function of the Botnet is to send as much packet to the victim as they can until the victim reaches the state where it became unresponsive and unable to reply to any request for services.

Although it seems a simple task but certainly it requires some effort from the attacker. The attacker needs to get Botnet and configure them as of the need ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “author” : { “dropping-particle” : “”, “family” : “Gul”, “given” : “Junaid”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Mushtaq”, “given” : “Sammee”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Riaz”, “given” : “Rabia”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “id” : “ITEM-1”, “issue” : “6”, “issued” : { “date-parts” : “2012” }, “page” : “87-92”, “title” : “OPTIMAL GUARD NODE PLACEMENT USING SGLD and ENERGY FACTOR”, “type” : “article-journal”, “volume” : “4” }, “uris” : “http://www.mendeley.com/documents/?uuid=d4b36f3a-55b7-49ab-83e3-7ee78dda7516” } , “mendeley” : { “formattedCitation” : “19”, “plainTextFormattedCitation” : “19”, “previouslyFormattedCitation” : “19” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }19.

The DDoS attack is a DoS attack that is generated by many distributed sources at the same time. The attackers use worms and viruses in order to infect the devices that will be used in the attack without trigger their owners intention. The adversary aims to build a network of these compromised machines to be under his control so he can manage each one of them to infect other devices in order to augment the number of attack sources. These devices are called bots and their network is called botnet. The attacker can use this botnet to launch several types of attacks including DDoS attacks. Currently, tools such as TFN can be used to launch DDoS attacks. There are many solutions have been proposed to encounter the DDoS attacks including: Overlay-based mitigation techniques that employ distributed firewalls and hide the protected system’s identity Push-back methods that use routers close to the sources for filtering purposes.

Mirai
A Mirai botnet is comprised of four major components. The bot is the malware that infects devices. Its twofold aim is to propagate the infection to misconfigured devices and to attack a target server as soon as it receives the corresponding command from the person controlling the bot, or botmaster. The command and control (C;C) server provides the botmaster with a centralized management interface to check the botnet’s condition and orchestrate new DDoS attacks. Typically, communication with other parts of the infrastructure is conducted via the anonymous Tor network. The loader facilitates the dissemination of executables targeting different platforms (18 in total, including ARM, MIPS, and x86) by directly communicating with new victims. The report server maintains a database with details about all devices in the botnet. Newly infected ones typically directly communicate with it.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/MC.2017.201”, “ISBN” : “978-1-5090-4862-5”, “ISSN” : “00189162”, “abstract” : “The Mirai botnet and its variants and imitators are a wake-up call to the industry to better secure Internet of Things devices or risk exposing the Internet infrastructure to increasingly disruptive distributed denial-of-service attacks.”, “author” : { “dropping-particle” : “”, “family” : “Kolias”, “given” : “Constantinos”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kambourakis”, “given” : “Georgios”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Stavrou”, “given” : “Angelos”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Voas”, “given” : “Jeffrey”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Computer”, “id” : “ITEM-1”, “issue” : “7”, “issued” : { “date-parts” : “2017” }, “page” : “80-84”, “title” : “DDoS in the IoT: Mirai and other botnets”, “type” : “article-journal”, “volume” : “50” }, “uris” : “http://www.mendeley.com/documents/?uuid=ecca3839-0556-4d33-8971-68d21479bdf7” } , “mendeley” : { “formattedCitation” : “20”, “plainTextFormattedCitation” : “20”, “previouslyFormattedCitation” : “20” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }20
Kambourakis et al discuss briefly about the Mirai and how IoT zombies workADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/MILCOM.2017.8170867”, “ISBN” : “9781538605950”, “author” : { “dropping-particle” : “”, “family” : “Kambourakis”, “given” : “Georgios”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kolias”, “given” : “Constantinos”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Stavrou”, “given” : “Angelos”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Proceedings – IEEE Military Communications Conference MILCOM”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2017” }, “page” : “267-272”, “title” : “The Mirai botnet and the IoT Zombie Armies”, “type” : “paper-conference”, “volume” : “2017-Octob” }, “uris” : “http://www.mendeley.com/documents/?uuid=80f13391-d24b-4173-910c-61d3b7456711” } , “mendeley” : { “formattedCitation” : “21”, “plainTextFormattedCitation” : “21”, “previouslyFormattedCitation” : “21” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }21. Mirai doesn’t try to avoid detection. Almost all stages of infection leave a footprint that can be recognized through basic network analysis. Mirai signatures include
sequentially testing specific credentials in specific ports,
sending reports that generate distinctive patterns,
downloading a specific type of binary code,
exchanging keep-alive messages,
receiving attack commands that have a specific structure, and
Generating attack traffic with very few random elements.

left18899300Results of DDoS per year
center226800
DDoS as a service
Security world observe number of DDoS attack in recent years. Usually DDoS attack is launched by bonet and other devices which require little effort. But, the nature of DDoS is also changing from simple to complex. From network level to application level. This could be result where application might be unavailable to the user. The main idea behind these attacks is to blackmail the organization and ask for money. As this happen people start providing DDoS as a service which might be a reasonable businessADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/MCOM.2017.1600980”, “ISSN” : “01636804”, “abstract” : “u00a9 2017 IEEE. In recent years, we have observed a resurgence of DDoS attacks. These attacks often exploit vulnerable servers (e.g., DNS and NTP) to produce large amounts of traffic with little effort. However, we have also observed the appearance of application-level DDoS attacks, which leverage corner cases in the logic of an application in order to severely reduce the availability of the provided service. In both cases, these attacks are used to extort a ransom, to hurt a target organization, or to gain some tactical advantage. As it has happened for many of the components in the underground economy, DDoS has been commoditized, and DDoS as a service (DaaS) providers allow paying customers to buy and direct attacks against specific targets. In this article, we present a measurement study of 17 different DaaS providers, in which we analyzed the different techniques used to launch DDoS attacks, as well as the infrastructure leveraged in order to carry out the attacks. Results show a growing market of short-lived providers, where DDoS attacks are available at low cost (tens of dollars) and capable of easily disrupting connections of over 1.4 Gb/s. In our study, particular attention was given to characterize application-level (HTTP) DDoS attacks, which are more difficult to study given the low volume of traffic they generate and the need to study the logic of the application providing the target service.”, “author” : { “dropping-particle” : “”, “family” : “Zand”, “given” : “Ali”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Modelo-Howard”, “given” : “Gaspar”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Tongaonkar”, “given” : “Alok”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Lee”, “given” : “Sung Ju”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Kruegel”, “given” : “Christopher”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Vigna”, “given” : “Giovanni”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “IEEE Communications Magazine”, “id” : “ITEM-1”, “issue” : “7”, “issued” : { “date-parts” : “2017” }, “page” : “14-21”, “title” : “Demystifying DDoS as a service”, “type” : “article-journal”, “volume” : “55” }, “uris” : “http://www.mendeley.com/documents/?uuid=3306f953-b6b4-4ecb-9037-8ed3f839fc97” } , “mendeley” : { “formattedCitation” : “22”, “plainTextFormattedCitation” : “22”, “previouslyFormattedCitation” : “22” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }22.

Big Data Analytics for DDoS
Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/MSP.2013.138”, “ISBN” : “1540-7993”, “ISSN” : “1540-7993”, “abstract” : “Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.”, “author” : { “dropping-particle” : “”, “family” : “Cardenas”, “given” : “Alvaro a.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Manadhata”, “given” : “Pratyusa K.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Rajan”, “given” : “Sreeranga P.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “IEEE Security & Privacy”, “id” : “ITEM-1”, “issue” : “6”, “issued” : { “date-parts” : “2013” }, “page” : “74-76”, “title” : “Big Data Analytics for Security”, “type” : “article-journal”, “volume” : “11” }, “uris” : “http://www.mendeley.com/documents/?uuid=a5a2a0f3-6440-478e-bbdf-7c6a35c30b43” } , “mendeley” : { “formattedCitation” : “23”, “plainTextFormattedCitation” : “23”, “previouslyFormattedCitation” : “23” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }23
IoT
Internet of Things is new paradigm that changes world as we see it now. IoT enables devices machines to communicate with each other to get better results and understanding about the environment. Many countries spending a lot in IoT R;D. IoT is being most promising area in technology for the future. Researchers are studying the ways of developing and deploying the IoT Based devices up to another level .From homogeneous to heterogeneous level communicationADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.bushor.2015.03.008”, “ISBN” : “0007-6813”, “ISSN” : “00076813”, “abstract” : “The Internet of Things (IoT), also called the Internet of Everything or the Industrial Internet, is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other. The IoT is recognized as one of the most important areas of future technology and is gaining vast attention from a wide range of industries. This article presents five IoT technologies that are essential in the deployment of successful IoT-based products and services and discusses three IoT categories for enterprise applications used to enhance customer value. In addition, it examines the net present value method and the real option approach widely used in the justification of technology projects and illustrates how the real option approach can be applied for IoT investment. Finally, this article discusses five technical and managerial challenges.”, “author” : { “dropping-particle” : “”, “family” : “Lee”, “given” : “In”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Lee”, “given” : “Kyoochun”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Business Horizons”, “id” : “ITEM-1”, “issue” : “4”, “issued” : { “date-parts” : “2015” }, “page” : “431-440”, “title” : “The Internet of Things (IoT): Applications, investments, and challenges for enterprises”, “type” : “article-journal”, “volume” : “58” }, “uris” : “http://www.mendeley.com/documents/?uuid=710f4103-edab-4dd8-bcd5-4cad08e9172f” } , “mendeley” : { “formattedCitation” : “24”, “plainTextFormattedCitation” : “24”, “previouslyFormattedCitation” : “24” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }24.

IoT and Big Data
Voluminous amounts of data have been produced since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This study investigates state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this study adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Lastly, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directionsADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/ACCESS.2017.2689040”, “ISBN” : “2169-3536 VO – PP”, “ISSN” : “21693536”, “abstract” : “Voluminous amounts of data have been produced since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This study investigates state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this study adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Lastly, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.”, “author” : { “dropping-particle” : “”, “family” : “Marjani”, “given” : “Mohsen”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Nasaruddin”, “given” : “Fariza”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Gani”, “given” : “Abdullah”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Karim”, “given” : “Ahmad”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hashem”, “given” : “Ibrahim Abaker Targio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Siddiqa”, “given” : “Aisha”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Yaqoob”, “given” : “Ibrar”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “IEEE Access”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2017” }, “page” : “5247-5261”, “title” : “Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges”, “type” : “article-journal”, “volume” : “5” }, “uris” : “http://www.mendeley.com/documents/?uuid=8b0e8f1f-f9ec-4b15-a742-25a1640fea22” } , “mendeley” : { “formattedCitation” : “25”, “plainTextFormattedCitation” : “25”, “previouslyFormattedCitation” : “25” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }25.

The radical evolution of internet into a network of interconnected objects that create a smart environment is characterized by the term Internet of Things (IoT). The adoption of IoT in manufacturing enables the transition of tradition manufacturing systems into modern digitalized ones, generating significant economic opportunities through industries re-shaping. Industrial IoT empowers the modern companies to adopt new data-driven strategies and handle the global competitive pressure more easily. However, the adoption of IoT, increases the total volume of the generated data transforming the industrial data into industrial Big Data. The work demonstrated in this paper presents how the adoption of IoT in manufacturing, considering sensory systems and mobile devices, will generate industrial Big Data. Moreover, a developed IoT application is presented showing how real industrial data can be generated leading to Industrial Big Data. The proposed methodology is validated in a real life case study from a mould-making industry.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.procir.2016.07.038”, “ISBN” : “22128271”, “ISSN” : “22128271”, “abstract” : “The radical evolution of internet into a network of interconnected objects that create a smart environment is characterized by the term Internet of Things (IoT). The adoption of IoT in manufacturing enables the transition of tradition manufacturing systems into modern digitalized ones, generating significant economic opportunities through industries re-shaping. Industrial IoT empowers the modern companies to adopt new data-driven strategies and handle the global competitive pressure more easily. However, the adoption of IoT, increases the total volume of the generated data transforming the industrial data into industrial Big Data. The work demonstrated in this paper presents how the adoption of IoT in manufacturing, considering sensory systems and mobile devices, will generate industrial Big Data. Moreover, a developed IoT application is presented showing how real industrial data can be generated leading to Industrial Big Data. The proposed methodology is validated in a real life case study from a mould-making industry.”, “author” : { “dropping-particle” : “”, “family” : “Mourtzis”, “given” : “D.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Vlachou”, “given” : “E.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Milas”, “given” : “N.”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Procedia CIRP”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2016” }, “page” : “290-295”, “title” : “Industrial Big Data as a Result of IoT Adoption in Manufacturing”, “type” : “paper-conference”, “volume” : “55” }, “uris” : “http://www.mendeley.com/documents/?uuid=d9b5d2a8-eeea-4bb9-8310-c8b1414142c1” } , “mendeley” : { “formattedCitation” : “26”, “plainTextFormattedCitation” : “26”, “previouslyFormattedCitation” : “26” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }26.

The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1016/j.comnet.2017.06.013”, “ISBN” : “9789897582554”, “ISSN” : “13891286”, “abstract” : “The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions.”, “author” : { “dropping-particle” : “”, “family” : “Ahmed”, “given” : “Ejaz”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Yaqoob”, “given” : “Ibrar”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hashem”, “given” : “Ibrahim Abaker Targio”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Khan”, “given” : “Imran”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Ahmed”, “given” : “Abdelmuttlib Ibrahim Abdalla”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Imran”, “given” : “Muhammad”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “V.”, “family” : “Vasilakos”, “given” : “Athanasios”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Computer Networks”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2017” }, “page” : “459-471”, “title” : “The role of big data analytics in Internet of Things”, “type” : “article-journal”, “volume” : “129” }, “uris” : “http://www.mendeley.com/documents/?uuid=80dee6b8-9699-417c-8220-125dfab7bb3b” } , “mendeley” : { “formattedCitation” : “27”, “plainTextFormattedCitation” : “27”, “previouslyFormattedCitation” : “27” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }27.

IoT as cyber weapon
The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-Time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-Attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/FAS-W.2016.58”, “ISBN” : “9781509036516”, “abstract” : “u00a9 2016 IEEE.The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-Time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-Attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.”, “author” : { “dropping-particle” : “”, “family” : “Pacheco”, “given” : “Jesus”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Hariri”, “given” : “Salim”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Proceedings – IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2016” }, “page” : “242-247”, “title” : “IoT security framework for smart cyber infrastructures”, “type” : “paper-conference” }, “uris” : “http://www.mendeley.com/documents/?uuid=e9a7c45f-6249-4c31-9c9d-12038874f1c9” } , “mendeley” : { “formattedCitation” : “28”, “plainTextFormattedCitation” : “28”, “previouslyFormattedCitation” : “28” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }28
We are currently living in the post-PC era where smartphones and other wireless handheld devices are changing our environment, making it more interactive, adaptive and informative. Termed as Internet of Things (IoT) evolving into Internet of Everything, the new ecosystem combines wireless sensor networks, cloud computing, analytical data, interactive technologies, as well as smart devices, to provision solutions in which the objects are embedded with network connectivity and an identifier to enhance object-to-object interactions. IoT innovation is advancing and provides diverse smart solutions or applications. From e-transport to e-health; smart living to e-manufacturing and many other e-solutions. In this environment, the rising trend of cyber attacks on systems infrastructure coupled with the system inherent vulnerabilities presents a source of concern not only to the vendors, but also to the consumer. These security concerns need to be addressed in order to ensure user confidence so as to promote wide acceptance and reap the potentials of IoT. From the perspectives of firmware, hardware and software infrastructure setups, this paper looks at some of the major IoT application and service domains, and analyze the cybersecurity challenges which are likely to drive IoT research in the near future.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1007/s11277-017-4434-6”, “ISBN” : “0929-6212, 1572-834X”, “ISSN” : “1572834X”, “abstract” : “We are currently living in the post-PC era where smartphones and other wireless handheld devices are changing our environment, making it more interactive, adaptive and informative. Termed as Internet of Things (IoT) evolving into Internet of Everything, the new ecosystem combines wireless sensor networks, cloud computing, analytical data, interactive technologies, as well as smart devices, to provision solutions in which the objects are embedded with network connectivity and an identifier to enhance object-to-object interactions. IoT innovation is advancing and provides diverse smart solutions or applications. From e-transport to e-health; smart living to e-manufacturing and many other e-solutions. In this environment, the rising trend of cyber attacks on systems infrastructure coupled with the system inherent vulnerabilities presents a source of concern not only to the vendors, but also to the consumer. These security concerns need to be addressed in order to ensure user confidence so as to promote wide acceptance and reap the potentials of IoT. From the perspectives of firmware, hardware and software infrastructure setups, this paper looks at some of the major IoT application and service domains, and analyze the cybersecurity challenges which are likely to drive IoT research in the near future.”, “author” : { “dropping-particle” : “”, “family” : “Tweneboah-Koduah”, “given” : “Samuel”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Skouby”, “given” : “Knud Erik”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “Tadayoni”, “given” : “Reza”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “Wireless Personal Communications”, “id” : “ITEM-1”, “issue” : “1”, “issued” : { “date-parts” : “2017” }, “page” : “169-185”, “title” : “Cyber Security Threats to IoT Applications and Service Domains”, “type” : “article-journal”, “volume” : “95” }, “uris” : “http://www.mendeley.com/documents/?uuid=ba1ee7cb-696b-4f70-abc6-6412f219010b” } , “mendeley” : { “formattedCitation” : “29”, “plainTextFormattedCitation” : “29”, “previouslyFormattedCitation” : “29” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }29
Social Internet of Things (SIoT) is a new paradigm where IoT merges with Social Networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem”. In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: Perception, Transportation and Application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions. Index terms: Internet of Things, IoT System Model, Cyber Threats, Trust, IoT Security, IoT Protocols.ADDIN CSL_CITATION { “citationItems” : { “id” : “ITEM-1”, “itemData” : { “DOI” : “10.1109/JIOT.2017.2767291”, “ISSN” : “23274662”, “abstract” : “Social Internet of Things (SIoT) is a new paradigm where IoT merges with Social Networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem”. In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: Perception, Transportation and Application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions. Index terms: Internet of Things, IoT System Model, Cyber Threats, Trust, IoT Security, IoT Protocols.”, “author” : { “dropping-particle” : “”, “family” : “FRUSTACI”, “given” : “Mario”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “PACE”, “given” : “Pasquale”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “ALOI”, “given” : “Gianluca”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” }, { “dropping-particle” : “”, “family” : “FORTINO”, “given” : “Giancarlo”, “non-dropping-particle” : “”, “parse-names” : false, “suffix” : “” } , “container-title” : “IEEE Internet of Things Journal”, “id” : “ITEM-1”, “issued” : { “date-parts” : “2017” }, “title” : “Evaluating critical security issues of the IoT world: Present and Future challenges”, “type” : “article-newspaper” }, “uris” : “http://www.mendeley.com/documents/?uuid=ee7ddbdf-f23e-4898-96dd-ffa5c885c3ce” } , “mendeley” : { “formattedCitation” : “30”, “plainTextFormattedCitation” : “30”, “previouslyFormattedCitation” : “30” }, “properties” : { }, “schema” : “https://github.com/citation-style-language/schema/raw/master/csl-citation.json” }30
left23114001016363038074Figure 1 Proposed Architecture
Figure 1 Proposed Architecture
Proposed Framework
Modified Fog, NFV, SDN Framework
SDN and NFV is modified to accommodate Blockchain properties. NFV uses servers and other commodities which opens ways for Blockchain to be introduced properly. In this modified frame work we suggest to make Blockchain of NFV with one or with FOG nodes that helps to secure their assets from illegal modification.

As in the figure above shows NFV part other network will perform analysis and other function but it also prone to attacks. In our framework we consider ever node and server as fog node that have certain capabilities. As these servers have the storage / capacity, we can effectively use Blockchain properties.
PacketIn
41910041402000Packet In is the procedure by which switchsendsd packet to the controller in openflow architecture.
Reason filed in header provides information why to send packet to a controller r that may be because of three reason defined in open flow architecture. One reason could be NoMatch have hex code 0x00, second is Action has a code
48895013335000×01 and the third is Invalid TTL with 0x02 code. Packet have matched and reason that we have used to identify the nature of the packet. It will be send to the SDN controller Distributed Denial of Service attack
Blockchain
Blockchain basically a ledger that record all transaction made by authenticated users while utilizing the power of smart contact and certain hashes. Blockchain initially introduced by a Japanese hacker. Concept of the Blockchain is to save information in decentralized way so no one can modify certain record without knowing.

Smart Contract
AS we are going to build private Blockchain, smart contract will be initial point on which cloud ; fog are agreed. For communication between Fog to Fog, we need cluster head to communicate with each other.

Smart contract are digital form of terms and conditions. It has information about what and how much privileges certain party has.

Calculating Hash ; Sharing
Hashes are back bone of Blockchain. Hashes are used to add, delete and for every communication that occur in block chain. For our algorithm and framework, we choose SHA256 hash family. Hash will be shared across the Blockchain and saved in the ledger. Smart contract also need this hash information for future if any modification is required.

Adding ; deleting block
We are developing private Blockchain so, adding or deleting Block function is assigned to cloud or Fog on special duty. Fog on special duty and its responsibilities is explain in further section.

If cloud want to add new Fog node then it will generate smart contract accordingly and share it with Blockchain, then the information will be shared to every Node. These node keep the ledger until the smart contract is revoked.

Algorithm
Step1:
Gn C
If(Gn==Buisness Rules)
{
Accepted(Gn);
Acknowlge(Gn);
Goto to step2
}
Else
{
Reject and acknowledge(Gn);
}
Step2:
Add(Gn);
Create Sm(Gn)Bl
Step3:
Gn Request for DbBl
Step4:
Bool Result = Bl_authentication(Sm_Gn);
If (Result == 1)
{
SahreDB(Gn); loop until DB shared
}
Else
{
Reject_Request(Gn);
Goto step 5;
}
Step5:
Finish();
Save and Exchange information
As, one of our main focus is DDoS and it’s after effect on the network. So, we have to secure SDN and NFV from illegal modification. The modification is referred as “configuration updation”. Any change made by cloud is a transaction and recorded in the ledger according to the smart contract.

Figure 2 Activity Diagram
Fog Node on special duty:
We introduce a new term “fog node on special duty”. This node is vital if different type of fog need to increase their security. This service could be provided by any residing fog.

This special node is selected by check certain parameters like bandwidth, power, storage capacity etc. To do this, new algorithm is required that can choose the Special node, create new smart contract on which both parties can agree and work accordingly to secure each other’s assets.
Algorithm
Step1
C1, C2 ? Cn
Initial hand Shake between C1 ; C2
Step 2
CreateSm(c1,c2); c1_sm, c2_sm
Share Sm(C1,C2);
Step 3
If (c1_sm==c2sm)
{
Calculate Fn probability ();
Fn Bl
}
Step 4
Create Sm (Fn_S_C1C2);
Step 5
Share (Fn_S_C1C2) with involved parties
If (Fn_S_C1C2 is not revoked)
{
Fn Start sharing DB
}
Else
{
Go to step 6
}
Step 6
Finish ();

Cluster head Selection Probability
PX=x and Y=y=PY=yX=x.PX=x =PX=xY=y.P(Y=y)Where PY=yX=x is the probability of Y=y given that X=xThe generalization of the preceding two variable case is the joint probability distribution of n discrete random variables k1,k2,k3,………,knP=X1=k1,X2=k2,…,Xn=kn=PX1=k1×PX2=k2X1=k1 ×PX3=k3X1=k1,X2=k2 … ×P(Xn=kn|X1=k1,X2=k2,…,Xn-1=kn-1)N= k1,k2,k3,…, knwhere kn?N is the finite number of nodes in a fogEach of the node in N has either computational power, storage capacity or both according to the usage of the nodes.
Sc=storage capacity ; Cp=computational powerSc=0,1, Cp=0,1 are the states of each nodeSome nodes can have both qualities (i.e. storage capacity and computational power) and either.

Suppose
Sc=1, ;if kn has Storage capacity0, ; Otherwise Cp=1, ;if kn has Computational power0, ; Otherwisek1 k2k3k4,…,knSc010101Cp100100fNk=kSc,cp=1N if Sc,Cp=1kSc,cp=0N if Sc,Cp=01-kSc,cp=0N-kSc,cp=1N otherwiseP(not chosen)=Sc=0,Cp=0=kSc,cp=0NP(others)=Sc=0,Cp=1 or Sc=1,Cp=0 =kSc,cp=0,1NP(exp. candidate)=Sc=1,Cp=1=kSc,cp=1NPnot candidate=Pnot chosen+Pothers =kSc,cp=0N+kSc,cp=0,1N or = 1-kSc,cp=1NProbability of choosing cluster head among the nodes which are expected candidates.

Pc.h=ki= kSc,cp=1NThe probability of the next cluster head will be calculated by following equation
Pc.h=kic.h=ki-1)= (kSc,cp=1)-1N-1Pc.h=ki-1?c.h=ki=Pc.h=ki× Pc.h=kic.h=ki-1 = kSc,cp=1N×(kSc,cp=1)-1N-1Number of occurrence ScScSum
CpkSc=1,cp=1kSc=0,cp=1kSc=1,cp=1+kSc=0,cp=1CpkSc=1,cp=0kSc=0,cp=0kSc=1,cp=0+kSc=0,cp=0Sum kSc=1,cp=1+kSc=1,cp=0kSc=0,cp=1+kSc=0,cp=0NPch=nP(kSc,cp=1?(N-kSc,cp=1))n =nPkSc,cp=1 N-kSc,cp=1n)P((N-kSc,cp=1)n)Blockchain Expansion Algo
Step1:
Initial Hand Shake between C3
C1,C2,C3 Cn
Step2:
Check If(C1,C2,C3 already have Sm)
{
GotoStep 3
}
Else
{
Ctreate Sm(Accordingly for Cn);
}
Step3:
Get Cluster head from Algorithm Probability();
Step4:
Share Sm With F ? Gb23 ; F ? Gb12
Step5:
create Bl(GB123);
ShareDb(Sm_C123);
Step6:
Do until Sm_C123 is revoked
Finish();
Methodology
Incoming data will be received by the FOG layer via common networking technologies comprises of general purpose networking hardware like hub, switches, routers, etc. As data reaches to Fog layer, it will serve as a first line of defense. This first line of defense requires to defend its assets (hardware and software configuration) against the attack besides defending the cloud. To defend Fog layer SND and NVF are introduced as a networking component. SDN and NVF provide security to the network. Then Block chain technology is embedded in the Fog layer to protect Fog layer from attacks along with DDoS.

Blockchain can be created among the nodes in NFV or from third party. It requires smart contract from the including parties and to be shared with nodes. Nodes should have some capabilities to serve as a Blockchain node that is why cluster head selection probability should be calculated.

Once we select our node for Blockchain smart contact from the cloud will provide all the required information so Blockchain can be established among the involved parties.

Conclusion
Blockchain in Fog architecture along with SDN and NFV prove to be very secure. Also, because of Fog architecture property response time is very low. This means localizing DDoS attack while maintaining the security with provided architecture is up to the mark. The expansion algorithm opens the ways to create better and secure Blockchain while using Fog architecture. As 5G prove to be faster than 4g because of SDN and NFV, this concludes provided framework is not only fast, but because of Blockchain its more secure and easy to maintain.

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