A Brief Analysis on Efficient Machine Learning Techniques for Intrusion Detection Model to Provide Network Security

An Intrusion Detection System (IDS) is a software used to monitor a single computer or a network of computers to combat network attacks, block access to certain websites, or otherwise the attack on network protocols. The dynamic and complicated nature of cyber attacks on computer systems is beyond the capabilities of the majority of current IDS solutions. By combating intrusion attempts, network security professionals ensure that the services can be accessed at all times. One of the tools at disposal to detect and categorize suspicious behaviour is IDS. For almost 40 years, researchers in both academia and industry have been proposing strategies for detecting and avoiding such security breaches, and even constructing systems to do so. Intrusion detection systems that rely on attack signatures examine network traffic for suspicious activity. Anomaly-based intrusion detection systems, on the other hand, build a model to tell good users apart from bad ones, allowing them to spot previously unseen attacks. Machine learning (ML) techniques are increasingly being employed as a means of categorizing normal and abnormal activities. Many other ML-based intrusion detection systems have been proposed in the past few years. The purpose of this work is to provide a critical overview of the state of the art in machine learning (ML)-based intrusion detection methods. Researchers focusing on ML-based systems that detect intrusions could use this survey as a supplement to previous generic surveys on intrusion detection and a source to recent work done in the area.