Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
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Abstract (arXiv)
Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training, and eXplainable AI to ensure interpretability and trust, which are vital in cybersecurity. This paper presents an interesting review of current AI-based cybersecurity trends, focusing on intrusion detection approaches and aiming to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.
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References · 4
- 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
- 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
- 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
- 2021Towards a Standard Feature Set for Network Intrusion Detection System Datasetscited by 40
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