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Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning
Amazon, Seattle, WA 98109 USA..
Amazon, Seattle, WA 98109 USA..
Daffodil Int Univ, Dept Software Engn, Dhaka 1216, Bangladesh..
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 63584-63597Article in journal (Refereed) Published
Abstract [en]

The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service, Distributed Denial of Service, Brute Force, Man-in-the-Middle, and Replay Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion Detection Rate (DR) and the False Alarm Rate (FAR), facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% at the 70% threshold of DR-FAR trade-off and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected sub-networks, thereby positioning Deep-IDS as a leading IDS for IoT-enhanced computer networks.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2024. Vol. 12, p. 63584-63597
Keywords [en]
Internet of Things, Long short term memory, Mathematical models, Intrusion detection, Logic gates, Recurrent neural networks, Real-time systems, Network security, Deep learning, intrusion-detection system (IDS), Internet of Things (IoT), LSTM, response mechanism, intrusion detection rate
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-66651DOI: 10.1109/ACCESS.2024.3396461ISI: 001216600900001Scopus ID: 2-s2.0-85192165604OAI: oai:DiVA.org:mdh-66651DiVA, id: diva2:1859562
Available from: 2024-05-22 Created: 2024-05-22 Last updated: 2024-05-22Bibliographically approved

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Kabir, Md Alamgir

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