Improving cybersecurity with random forest algorithm-based big data intrusion detection system: A performance analysis
Alaa Abd Ali Hade, Amjad Mahmood Hadi, Amjad Mahmood Hadi, Amjad Mahmood Hadi · AIP conference proceedings · 2024
Even security specialists find it challenging to monitor the complex interconnections of computers and network devices brought about by the expansion of the internet over the past ten years.Network security has grown to be a major problem as personal computers have become faster and highspeed internet has become more widely accessible.It is extremely difficult to create intrusion detection systems that can manage massive amounts of data, especially in terms of system construction time.This work suggests a preprocessing feature selection strategy that creates subsets of pertinent characteristics to ease model construction in order to overcome this difficulty.The suggested model uses the information gain method to improve accuracy while classifying network data using the Random Forest algorithm.Using the NSL-KDD reference dataset, the suggested model's efficacy is assessed.Several measures are used to determine how well it performs.According on empirical findings, the recommended model outperforms existing algorithms in terms of performance measures.It offers a contrast.Overall, the proposed methodology has a great deal of promise to enhance large data intrusion detection systems' functionality.