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Application of AI in Cyberattack Detection: A Review.

Yaw Jantuah Boateng¹, Nusrat Jahan Mim², Nasrin Akhter³

¹Faculty of Physical and Computational Sciences, Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi P.O. Box KS5013, Ghana.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

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This summary is machine-generated.

Artificial Intelligence (AI) offers advanced solutions for detecting sophisticated cyberattacks in digital systems. This review explores AI techniques like Machine Learning (ML) and Deep Learning (DL) for enhanced cybersecurity.

Area of Science:

Cybersecurity and Artificial Intelligence
Intrusion Detection Systems
Machine Learning and Deep Learning Applications

Background:

Cyber-physical systems face increasing security threats from advanced cyberattacks.
Traditional signature-based Intrusion Detection Systems (IDS) struggle against novel and zero-day attacks.
Artificial Intelligence (AI) presents scalable, accurate, and adaptive solutions for modern cyberattack detection.

Purpose of the Study:

To comprehensively review recent advancements in AI-based cyberattack detection techniques.
To evaluate the strengths, limitations, and performance of various AI approaches on benchmark datasets.
To identify key challenges and future research directions in AI-driven cybersecurity.

Main Methods:

Review of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL).

Keywords:

artificial intelligence cyberattack deep learning explainable AI federated learning intrusion detection systems quantum computing reinforcement learning

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Exploration of emerging techniques: generative AI, neuro-symbolic AI, swarm intelligence, lightweight AI, and quantum computing.

Analysis of AI-driven anomaly-based and hybrid detection methods versus traditional signature-based IDS.

Main Results:

AI-driven methods, particularly anomaly-based and hybrid approaches, show improved detection rates for unknown and zero-day attacks.
Key challenges identified include computational costs, data quality, privacy, and model interpretability, with Explainable AI (XAI) offering solutions.
Lightweight AI and quantum computing show potential for resource-constrained environments and enhanced detection efficiency.

Conclusions:

AI is crucial for developing robust, interpretable, and efficient cyberattack detection systems.
Future research should focus on updated datasets, hybrid quantum-classical models, and optimized Federated Learning (FL) protocols.
Continued innovation in AI is essential for securing complex digital environments against evolving cyber threats.