NETWORK INTRUSION DETECTION SYSTEMS AND SECURITY UPGRADES USING MACHINE LEARNING TECHNIQUES

Abstract

Machine learning (ML) is a modern image-processing technique with high potential. It has recently entered the networking field since it has been eminently used in various areas. Currently, Machine learning is a new technology that can be used in the modern networking and communication sector; it’s applied to enhance the productivity and security of the privacy - Artificial Neural Networks (ANNs), now one of the most widely-known approaches to computational intelligence. IDS improves network security by aiding in the prevention of an increasing variety of attacks. NIDS can be categorised as either signature-based or anomaly-based. The anomaly-based NIDS, based on machine learning models and can identify attacks with high accuracy, is the most prominent type of NIDS. In recent years, artificial neural networks have various advantages in pattern recognition and machine learning. Machine learning is successful because it can quickly and efficiently find patterns and predict problems with enormous amounts of information. By automating the analysis, cyber teams may immediately identify threats and isolate situations that require additional human investigation. The network security environment has extensively used machine learning techniques, supervised learning, unsupervised learning, and reinforcement learning. Additionally, it provides concise explanations of each ML technique, often utilised security datasets, required ML tools, and assessment metrics for classification model evaluation. The issues of using ML approaches to cyber security are finally explored. This chapter on the application for machine learning in computer networking in the real-world framework. It provides classification techniques like neural networks (RNNs, CNN) other methods (SVM, KNN, and Decision Tree). It supplies the reader with knowledge of present and emerging mode in ML applications research and area of focus for researchers. Labeling network traffic or designing access control policies aims to fine-tune the many aspects of pertinent security procedures to mitigate a specific attack. This chapter is conducted research efforts that employ various neural networks such as convolution neural networks (CNN), which constitute a specific class of ML, applied to other networking and security issues challenges.