Averaged dependence estimators for DoS attack detection in IoT networks

• Published in: Future generation computer systems Vol. 102; pp. 198 - 209
• Main Authors: Baig, Zubair A., Sanguanpong, Surasak, Firdous, Syed Naeem, Vo, Van Nhan, Nguyen, Tri Gia, So-In, Chakchai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2020
• Subjects: Communication system security; Internet of Things; Machine learning algorithms; Internet of Things; Communication system security; Machine learning algorithms
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2019.08.007

Wireless sensor networks (WSNs) have evolved to become an integral part of the contemporary Internet of Things (IoT) paradigm. The sensor node activities of both sensing phenomena in their immediate environments and reporting their findings to a centralized base station (BS) have remained a core platform to sustain heterogeneous service-centric applications. However, the adversarial threat to the sensors of the IoT paradigm remains significant. Denial of service (DoS) attacks, comprising a large volume of network packets, targeting a given sensor node(s) of the network, may cripple routine operations and cause catastrophic losses to emergency services. This paper presents an intelligent DoS detection framework comprising modules for data generation, feature ranking and generation, and training and testing. The proposed framework is experimentally tested under actual IoT attack scenarios, and the accuracy of the results is greater than that of traditional classification techniques. •DoS attack detection for IoT platforms.•AODE-based classification of network traffic.•Machine learning and applications for network security in IoT on 5G networks.