Machine Learning Approaches for Network Attack Detection: A Comprehensive Review of Techniques, Challenges, and Future Directions

Internet-connected devices have continued to proliferate, and so has cyberspace, increasing the count and severity of cyber-attacks. This necessitated the improvement of network security mechanisms. Traditional detection systems may work to a certain extent but have not been able to identify advanced and evolving threats. On the other hand, machine learning has a great solution in detecting and mitigating network attack effects due to its ability to learn patterns and adapt to novel threats. This paper is about the study on the efficacy of machine learning in network intrusion recognition at highlighting the challenges presented by traditional techniques along with the advantages of resorting to machine learning approaches. It discusses different kinds of network attacks, their classification types, and their specific real-time detection methods while highlighting limitations such as high false-positive rates and an unmet demand for huge datasets. The review will also emphasize continuously updating data, as well as retraining the model for top-notch detection performance. Overall, the synergy of machine learning and network security frameworks holds a great promise in improving the cyber defence strategy in an increasingly convoluted digital domain.