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Semi-supervised learning based distributed attack detection framework for IoT - 2018

Semi-Supervised Learning Based Distributed Attack Detection Framework For Iot

Research Paper on Semi-Supervised Learning Based Distributed Attack Detection Framework For Iot

Research Area:  Machine Learning


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%.

Semi-Supervised Learning
Distributed Attack Detection
Machine Learning
Deep Learning

Author(s) Name:  Shailendra Rathore and Jong HyukPark

Journal name:  Applied Soft Computing

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.asoc.2018.05.049

Volume Information:  Volume 72, November 2018, Pages 79-89