A Study on Network Intrusion Detection Systems by Employing Memory-Based Learning Techniques

Computer networks must be shielded from numerous security threats and assaults. Network security can be improved by using systems that detect intrusions on networks. As security breaches become more sophisticated and frequent, there is an increasing demand for advanced analytics techniques to enhance NIDS's detection as well as reaction capabilities. The creation and application of analytics techniques for identifying intrusions on networks of computers is the main goal of this research. Applying these methods to NIDS will improve its ability to identify and handle security-related problems. The research begins with a discussion of the principles of discovering network intrusions, covering the many kinds of assaults and the difficulties in identifying them. Signature-based systems and anomaly-based systems, two common types of NIDS models, each have their own set of benefits and drawbacks, which are discussed here. The major goal of this software is to employ analytical methods to further improve network intrusion detection systems (NIDS). In the face of constantly developing cyber threats, the proposed approaches have the potential to improve attack detection accuracy, reduce false positives, enable efficient huge data processing, and facilitate automated incident response.