Metaheuristic algorithms in network intrusion detection

Hassan, Ibrahim Hayatu; Mohammed, Abdullahi; Masama, Mansur Aliyu

doi:10.1016/b978-0-323-91781-0.00006-5

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2024

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Cited by 4 publications

References 63 publications

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Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection

Manoharan,

Thathan

2024

Sci Rep

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Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection

Manoharan,

Thathan

2024

Sci Rep

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IoT Cyber Attacks Detection - Survey

Zubaidi

2024

2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

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Advancing cybersecurity: a comprehensive review of AI-driven detection techniques

Salem,

Azzam,

Emam

et al. 2024

J Big Data

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As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks.

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Metaheuristic algorithms in network intrusion detection

Cited by 4 publications

References 63 publications

Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection

Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection

IoT Cyber Attacks Detection - Survey

Advancing cybersecurity: a comprehensive review of AI-driven detection techniques

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