DDoS Detection and Alleviation in IoT using SDN (SDIoT-DDoS-DA)

Wani, Azka; Revathi, S.

doi:10.1007/s40031-020-00442-z

Cited by 22 publications

(14 citation statements)

References 16 publications

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“…In a WSN, devices at the edge are possible breakdown points where attacks like Distributed Denial-of-Service (DDoS) could be established [122], [123]. At the WSN edge, infected nodes could collaborate to crash the WSN service provision, as observed in the botnet attack in August 2016 [124]. In the latest attack, the Mirai botnet developed and raised the most potent attack in opposition to WSN security by compromising the WSN nodes and creating malicious traffic in the order of Tbps [124].…”

Section: Security Problems Of Centralized Wsn Modelsmentioning

confidence: 99%

“…At the WSN edge, infected nodes could collaborate to crash the WSN service provision, as observed in the botnet attack in August 2016 [124]. In the latest attack, the Mirai botnet developed and raised the most potent attack in opposition to WSN security by compromising the WSN nodes and creating malicious traffic in the order of Tbps [124]. Subsequently, after the Mirai botnet source code was openly released, an abundance of attacks followed, the most notable is October 2016 attack, which brought down numerous standard websites for many hours.…”

Section: Security Problems Of Centralized Wsn Modelsmentioning

confidence: 99%

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Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

et al. 2021

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Wireless Sensor Networks (WSNs) are broadly applied for various applications in tracking and surveillance due to their ease of use and other distinctive characteristics compelled by real-time cooperation among the sensor nodes. In WSNs, security is becoming a critical issue, as the techniques for malicious node detection adopt a one-time, centralized decision-making approach. With this paradigm, errors are difficult to avoid, and reproducibility and traceability are challenging. Hence, malicious node discovery technologies in conventional WSNs cannot assure traceability and fairness of the detection method. Herein, this paper discusses an in-depth survey of a blockchain-based approach for malicious node detection, an exhaustive examination of the integration of blockchain techniques with WSNs (BWSN), and insights into this novel concept. This survey discusses the architecture, sector-wise applications, and uses of BWSN. Moreover, this survey describes malicious node detection based on BWSN in two parts: 1) the BWSN architecture for detecting the malicious nodes and 2) the smart contract aspects in malicious node detection. Next, this survey explains the contributions of blockchain for WSN data management, which involves online information aggregation and may include auditing, event logs, and storage for information analysis and offline query processing. This survey first presents the conventional WSN solutions then the blockchain-based WSN solutions for data management. Additionally, this survey discusses the contributions of blockchain for WSN security management. It first examines the centralized WSN models for security problems, followed by a discussion of the blockchain-based WSN solutions for security management, such as offering access control, preserving information integrity, guaranteeing privacy, and ensuring WSNs' node longevity.

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Section: Security Problems Of Centralized Wsn Modelsmentioning

confidence: 99%

Section: Security Problems Of Centralized Wsn Modelsmentioning

confidence: 99%

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

et al. 2021

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“…The router strategy is extended by [14]. The paper demonstrates how to use a software-defined network architecture for edge DDoS protection, possibly leveraging infrastructure that is already in place.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems

Danicki

Haug

Behnke

et al. 2021

Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking

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Manufacturing, automotive, and aerospace environments use embedded systems for control and automation and need to fulfill strict real-time guarantees. To facilitate more efficient business processes and remote control, such devices are being connected to IP networks. Due to the difficulty in predicting network packets and the interrelated workloads of interrupt handlers and drivers, devices controlling time critical processes stand under the risk of missing process deadlines when under high network loads. Additionally, devices at the edge of large networks and the internet are subject to a high risk of load spikes and network packet overloads.In this paper, we investigate strategies to detect network packet overloads in real-time and present four approaches to adaptively mitigate local deadline misses. In addition to two strategies mitigating network bursts with and without hysteresis, we present and discuss two novel mitigation algorithms, called Budget and Queue Mitigation. In an experimental evaluation, all algorithms showed mitigating effects, with the Queue Mitigation strategy enabling most packet processing while preventing lateness of critical tasks.

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“…Wani et. al [45] proposes SD-IoT-based security mechanism for DDoS detection in SD-IoT network. This research uses MCOD and MLP approaches to identify the abnormal behavior of IoT networks.…”

Section: ) Data Planementioning

confidence: 99%

An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)

2020

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DDoS Detection and Alleviation in IoT using SDN (SDIoT-DDoS-DA)

Cited by 22 publications

References 16 publications

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems

An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)

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