AI-Based Approaches to Port Scan Detection in Network Security

The increasing sophistication of cyberattacks necessitates advanced methods of intrusion detection to safeguard modern networks. Port scanning, often the precursor to more complex cyber threats, remains one of the most commonly used techniques for identifying vulnerabilities in networked systems. This paper explores the role of artificial intelligence (AI) in enhancing the detection of port scan activities in network security. Traditional methods, such as signature-based and statistical approaches, have been instrumental but struggle to cope with emerging attack techniques. We examine AI-based solutions, including machine learning (ML), deep learning (DL), and anomaly detection models, focusing on their ability to improve the accuracy, efficiency, and scalability of port scan detection. The challenges associated with implementing AI in this domain, such as data quality, computational complexity, and interpretability, are also discussed. Finally, the paper highlights promising advancements, including reinforcement learning, federated learning, and the integration of threat intelligence, that are poised to drive the future of AI-based port scan detection systems. The ongoing research and technological innovations in AI hold significant promise for strengthening cybersecurity defenses and providing more robust protection against port scanning and other cyber threats.

کلیدواژه ها:

Port Scan Detection ، Artificial Intelligence (AI) ، ML ، Network Security