Edge-detect: Edge-centric network intrusion detection using deep neural network

Singh, P and Jishnu Jaykumar, P and Pankaj, A and Mitra, R (2021) Edge-detect: Edge-centric network intrusion detection using deep neural network. In: 2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021, 9-13 Jan 2021, Virtual, Las Vegas; United States.

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Abstract

Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing Network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate 'Edge-Detect' model, which detects Distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring Neural Network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99 even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Data Science; Deep neural networks; Denial-of-service attack; Intrusion detection; Lagrange multipliers; Network security, Distributed denial of service attack; Network infrastructure; Network intrusion detection; Network intrusion detection systems; Processing capability; Resource Constraint; Resource utilizations; Within resources, Recurrent neural networks
Department/Centre:	Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Date Deposited:	19 Apr 2021 11:16
Last Modified:	19 Apr 2021 11:16
URI:	http://eprints.iisc.ac.in/id/eprint/68642

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