Extreme SDN Framework for IoT and Mobile Applications Flexible Privacy at the Edge

Uddin, Mostafa; Nadeem, Tamer; Nukavarapu, Santosh Kumar

doi:10.1109/percom.2019.8767413

Cited by 19 publications

(13 citation statements)

References 27 publications

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“…As mentioned earlier, there are very few works that present a combined architecture including SDIoT [ 78 , 79 , 80 , 81 , 82 ] and SDIoT-Edge frameworks [ 83 , 84 ] suggested for effective management, increased scalability and interoperability. However, we note that (to the best of our knowledge) there are no works that study the heterogeneous service architecture that combines SDIoT-Edge with blockchain.…”

Section: Future Research Directionsmentioning

confidence: 99%

“…The increased number of connected devices poses several challenges e.g., to effectively handle the enormous amounts of data [ 62 , 63 ], interoperability among various hardware and software platforms [ 64 , 65 ], challenges related to Big data management, privacy and provenance [ 66 ], mobility management and handover [ 67 , 68 , 69 ], and privacy and security challenges [ 70 , 71 ]. We note that there are various efforts from the research community in order to improve the performance and scalability of the IoT netwroks e.g., the use of edge computing to perform resource-intensive tasks [ 72 , 73 , 74 ], the use of SDN for flexible management and programmability [ 75 , 76 ] including flexible network programmability [ 77 , 78 ] that introduced a new combination of Software-Defined Internet of Things (SDIoT) architecture for an effective management [ 78 , 79 , 80 , 81 , 82 ]; similarly, the Software-Defined Internet of Things and Edge (SDIoT-Edge) frameworks [ 83 , 84 ] were suggested for increased scalability and interoperability [ 82 ].…”

Section: Introductionmentioning

confidence: 99%

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Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey

Shah

Ullah

et al. 2022

Sensors

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Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.

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Section: Future Research Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey

Shah

Ullah

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…The traffic traces of different activities on a device can have different signatures. For example, based on our prior work, [17], Figure 1.a shows the different signatures for network traffic of different IoT devices. While different IoT devices have unique traffic signatures, the traffic signatures of similar devices are similar despite their different activities, as shown in Figure 1.b.…”

Section: A the Significance Of Iot Classificationmentioning

confidence: 99%

iKnight–Guarding IoT Infrastructure Using Generative Adversarial Networks

Nukavarapu

Nadeem

2022

IEEE Access

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With the phenomenal growth of IoT devices and their exponentially increasing applications at the edge of the network, it has become imminent to provide secure, flexible, and programmable network services to support user privacy, security, and quality of service. However, such services can only be enabled and effectively applied when the edge systems automatically identify these devices. Existing IoT classification systems are based on supervised training methods that require labeled data and manual feature extraction. Such approaches suffer from many challenges, such as privacy, labeling efforts, and adaptability to new devices. This paper develops a multistage multi-class classifier based on semi-supervised generative adversarial networks that perform automatic feature extraction with minimal labeled data. The classifier can identify devices with 96% accuracy when only 3% of the training data is labeled. Moreover, the classifier can correctly infer the device type (IoT, non-IoT, and anomaly) of any new device with 90% accuracy. We also show how our model can support various features such as noise robustness, continual learning ability, and novelty detection, such as zero-day malware attacks. Finally, we integrate our classifier with a softwaredefined network to enable smart IoT network services at the edge.

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“…PrivacyGuard [66] is a system designed and developed to obfuscate the activities of sensitive IoT and mobile applications from attacks over WiFi network. They have implemented a prototype this systems on Android mobile devices that to apply application level traffic shaping and IP-sec tunneling schemes.…”

Section: B Egde Computing Based Abr Systemsmentioning

confidence: 99%

“…Here they provide evidence of PHY layer metrics are used in assigning resources to the users in the cellular network. ABR Adaptive Bitrate Streaming (ABR) ABR based on ML [58,59] Based on DL [10,11,18,40,47] Based on RL [104] Egde computing based ABR systems [60,62,65] Using SDN [63,66] ABR with different optimization goals Improve QoE for vehicles [45,86,87,93] Optimize for user data-plan [67] Chunk Based Filtering [56] Optimizing video during handovers [45,93,96] Video stream in vehicular networks [85][86][87]105] 4K and 360 • video streaming 4K video streaming [51] 360 • video streaming [78,79,[82][83][84] Cross-layer optimizations for Video streaming [55,97,106,107] Fig. 6.…”

Section: Abr Algorithms With Cross-layer Optimizationsmentioning

confidence: 99%

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

Nishat,

Gnawali,

Abdelhadi

2020

Preprint

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In today's Internet, video is the most dominant application and in addition to this, wireless networks such as WiFi, Cellular, and Bluetooth have become ubiquitous. Hence, most of the Internet traffic is video over wireless nodes. There is a plethora of research to improve video streaming to achieve high Quality of Experience (QoE) over the Internet. Many of them focus on wireless nodes. Recent measurement studies often show QoE of video suffers in many wireless clients over the Internet. Recently, many research papers have presented models and schemes to optimize the Adaptive BitRate (ABR) based video streaming for wireless and mobile users. In this survey, we present a comprehensive overview of recent work in the area of Internet video specially designed for wireless network. Recent research has suggested that there are some new challenges added by the connectivity of clients through wireless. Also these challenges become more difficult to handle when these nodes are mobile. This survey also discusses new potential areas of future research due to the increasing scarcity of wireless spectrum.

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Extreme SDN Framework for IoT and Mobile Applications Flexible Privacy at the Edge

Cited by 19 publications

References 27 publications

Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey

Blockchain Based Solutions to Mitigate Distributed Denial of Service (DDoS) Attacks in the Internet of Things (IoT): A Survey

iKnight–Guarding IoT Infrastructure Using Generative Adversarial Networks

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey

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