Smart Monitoring and Intrusion Detection for Enhanced Real-Time Network Security

Intrusion detection systems (IDS) are becoming essential tools for security for the network due to the growing incidences and sophistication of cyber attacks. IDS identify odd trends in communication over the network and notify administrators of possible security breaches by continually reviewing the data. Conventional rule-based methods for detecting attacks have not been able to keep up with sophisticated adversaries. Machine learning has become more popular in the development of IDS systems as a solution to this. IDS is better equipped with machine learning to identify cyber threats quickly by employing advanced algorithms and machine learning to acquire knowledge from past data and adjust to changing assault techniques. Our goal is to develop a novel IDS utilizing state-of-the-art machine-learning methods, including SVM, k-NN, Decision Trees, Naive Bayes, and Neural Networks. Furthermore to these classification algorithms, ensemble approaches will be used to improve the safety of networks against dynamic cyber threats and raise the effectiveness of the classification techniques. NSL-KDD Benchmark Networking datasets will direct our investigation.