Semi-supervised learning based distributed attack detection framework for IoT

By S. Rathore, J. Park. Applied Soft Computing, 2018

297 citations recorded by the source corpus.

Abstract Alongside the development of Internet of Things (IoT), security attacks are also increasing day by day. A number of centralized attack detection mechanisms have been proposed to detect attacks in IoT, wherein an attack detection system is deployed at the central point in the network that collects data from the network and classifies it as “attack” or “normal” using a supervised machine learning algorithm. Note, however, that these mechanisms have failed to achieve significant results due to the distinct requirements of IoT devices, such as scalability, distribution, resource limitations, and low latency. Moreover, the application of supervised machine learning for classification needs a significant amount of labeled data. In this paper, we introduce a fog-based attack detection framework that relies on the fog computing paradigm and a newly proposed ELM-based Semi-supervised Fuzzy C-Means (ESFCM) method. As an extension of cloud computing, fog computing enables attack detection at the network edge and supports distributed attack detection. The ESFCM method uses a semi-supervised fuzzy c-means algorithm to handle the labeled data issue and an Extreme Learning Machine (ELM) algorithm to provide good generalization performance at a faster detection rate. The evaluation was performed on the NSL-KDD dataset, demonstrating that the proposed framework achieved better performance than the centralized attack detection framework. More specifically, it recorded a lower detection time of 11 milliseconds and an accuracy rate of 86.53%.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4fb38ea48756cbbf7acc05ff332e0b5d347f25ab

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Citations in the corpus

A review on extreme learning machine (2021, background): “Rathore and Park [148] proposed an ELM-based approach for detection of cyber-attack.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “An important advantage is that new online data can be incorporated in the learning process, which is not the case for ANN and NB. Rathore and Park [16] deployed a novel fog detector using ELM-based Semi-supervised Fuzzy C-Means (ESFCM) NIDS.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “Authors [193] in proposed an Extreme Learning Machine(ELM)-based Semi-Supervised Fuzzy CMeans (ESFCM) method that integrates a Semi-supervised”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “1) ML-Based Attack Detection and Mitigation in IoT: Rathore and Park [133] proposed a semi-supervised learningbased attack detection mechanism for IoT.”