A Survey of Data Mining and Machine Learning-Based Intrusion Detection System for Cyber Security
Sangeetha Ganesan, G. Shanmugaraj, A. Indumathi · Advances in information security, privacy, and ethics book series · 2023
With regard to intrusion detection systems (IDS), several research communities have shown interest in cyber security in recent years. IDS software keeps an eye out for malicious activity on a single computer or a network of computers. It is becoming increasingly important to detect intrusions and prevent them. Several methods to avoid or find intrusion in a network have been proposed in the past. However, the majority of the IDS detection methods currently in use are ineffective at solving this issue. Accuracy is the key factor in how well an intrusion detection system performs. In recent works, various techniques have been employed to enhance performance. In addition to this, machine learning (ML) has been used in many applications to produce results that are accurate in the relevant field. Determining how machine learning and data mining can be used to detect IDS in a network in the near future is the focus of this work. This literature review focuses on ML and data mining (DM) techniques for cyber analytics to support intrusion detection.