Securing Files on Cloud Storage with Group Key Management Protocol

Due to an exponential rise in the incidence of cyberattacks, there is a greater than ever need for enhanced Intrusion Detection Systems (IDS). In this aspect, the early classification of the attacks is greatly aided by the machine learning (ML) technique. Since there are numerous algorithms available, selecting the most suitable method was difficult. This study looked at some of the most cutting-edge intrusion detection methods and assessed their benefits and drawbacks in an effort to address this problem. Also, the best ML approach for classifying attacks is chosen from a selection of four ML methods. The effectiveness of the Collision detection system is assessed using a variety of methods that have been selected and tested. These Collision detection systems are divided into binary and multiclass attacks to verify whether the traffic has been classified as benign or an attack. The results show that the classification technique produces more accurate and consistent results. The experimental findings demonstrate that multi-classification improves performance in terms of intrusion detection, specifically allowing for a more targeted attack response.