ELD: Adaptive Detection of Malicious Nodes under Mix-Energy-Depleting-Attacks Using Edge Learning in IoT Networks

Zuchao Ma, Liang Liu, Weizhi Meng

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review


Due to the distributed framework, Internet of Things (IoT) is vulnerable to insider attacks like energy-depleting attack, where an attacker can behave maliciously to consume the battery of IoT devices. Such attack is difficult to detect because the attacker may behave differently under various environments and it is hard to decide the attack path. In this work, we focus on this challenge, and consider an advanced energy-depleting attack, called mix-energy-depleting attack, which combines three typical attacks such as carousel attack, flooding attack and replay attack. Regarding the detection, we propose an approach called Edge Learning Detection (ELD), which can learn malicious traffic by constructing an intrusion edge and can identify malicious nodes by building an intrusion graph. To overcome the problem that it is impractical to provide labeled data for system training in advance, our proposed ELD can train its model during detection by labeling traffic automatically. Then the obtained detection results can be used to optimize the adaptability of ELD in detecting practical attacks. In the evaluation, as compared with some similar methods, ELD can overall provide a better detection rate ranged from 5% to 40% according to concrete conditions.
Original languageEnglish
Title of host publicationInformation Security
Publication date2020
ISBN (Print)978-3-030-62973-1
Publication statusPublished - 2020
Event23rd International Conference on Information Security - Virtual event
Duration: 16 Dec 202020 Dec 2020


Conference23rd International Conference on Information Security
LocationVirtual event
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


  • IoT network
  • Malicious node
  • Insider attack
  • Edge learning
  • Mix-energy-depleting attack


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