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Network Intrusion Detection to Mitigate Jamming and Spoofing Attacks Using Federated Leading: A Comprehensive Survey
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Author(s): Tayyab Rehman (Air University, Pakistan), Noshina Tariq (Air University, Pakistan), Muhammad Ashraf (Air University, Pakistan)and Mamoona Humayun (Jouf University, Saudi Arabia)
Copyright: 2024
Pages: 24
Source title:
Cybersecurity Measures for Logistics Industry Framework
Source Author(s)/Editor(s): Noor Zaman Jhanjhi (School of Computing Science, Taylor’s University, Malaysia)and Imdad Ali Shah (School of Computing Science, Taylor’s University, Malaysia)
DOI: 10.4018/978-1-6684-7625-3.ch004
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Abstract
Network intrusions through jamming and spoofing attacks have become increasingly prevalent. The ability to detect such threats at early stages is necessary for preventing a successful attack from occurring. This survey chapter thoroughly overviews the demand for sophisticated intrusion detection systems (IDS) and how cutting-edge techniques, like federated learning-enabled IDS, can reduce privacy risks and protect confidential data during intrusion detection. It explores numerous mitigation strategies used to defend against these assaults, highlighting the significance of early detection and avoidance. The chapter comprehensively analyzes spoofing and jamming attacks, explores mitigation techniques, highlights challenges in implementing federated learning-based IDS, and compares diverse strategies for their real-world effects on network security. Lastly, it presents an unbiased evaluation of contemporary IDS techniques, assessing their advantages, disadvantages, and overall effect on network security while also discussing future challenges and prospects for academia and industry.
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