Integration Between Network Intrusion Detection and Machine Learning Techniques to Optimizing Network Security

In an increasingly linked world beset with cybersecurity risks, the necessity for powerful intrusion detection systems (IDS) is paramount. This thesis proposes a fresh approach to IDS development. using modern ma-chine learning algorithms and feature selection techniques to boost detection accuracy and resistance. Draw-ing upon lessons from earlier research, we address fundamental flaws in existing IDS approaches. emphasis on scalability and susceptibility to advanced assaults. Our suggested hybrid model, incorporating Random Forest, Gradient Boosting Machines, and Neural Networks, obtains a remarkable accuracy rate of 96% in identifying network intrusions. Utilizing the Intrusion Detection Evaluation Dataset (CIC-IDS2017), Our trials illustrate the efficacy of the proposed technique in real-world circumstances. This research contributes to the evolution of cybersecurity techniques by delivering practical insights for strengthening the security and resilience of digital infrastructures.