A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
- Title
- A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
- Creator
- Bebortta S.; Tripathy S.S.; Sharma V.; Behera J.R.; Nayak A.
- Description
- IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE.
- Source
- 2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep Q-Reinforcement Learning; Fog Computing; Internet of Things; Intrusion detection system; Performance Evaluation
- Coverage
- Bebortta S., Ravenshaw University, Department of Computer Science, Cuttack, 753003, India; Tripathy S.S., Kiit Deemed to Be University, School of Computer Engineering, Bhubaneswar, 751024, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR Campus, India; Behera J.R., Kiit Deemed to Be University, School of Electronics, Bhubaneswar, 751024, India; Nayak A., Driems University, Department of Computer Sceince & Engineering, Cuttack, 74022, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037378-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Bebortta S.; Tripathy S.S.; Sharma V.; Behera J.R.; Nayak A., “A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19123.