Transactions on Machine Intelligence 2821-1693 2 1 2019 03 01 Intelligent Intrusion Detection of Computer Networks Using Random Forest Algorithm 48 58 159732 10.47176/TMI.2019.48 EN S. Moshrefzadeh Department of Computer Engineering, Faculty of Technology and Engineering, Dana Institute of Higher Education, Yasouj, Iran. O. Rahmani Seryasat Department of Electrical Engineering, Faculty of Technology and Engineering, Shams Higher Education Institute, Gargan, Iran. 0000-0002-9289-6128 B. Ravaei Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj University, Yasouj, Iran. Journal Article 2018 10 22 Intelligent Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks against unauthorized access and cyber threats. These systems are engineered to detect, identify, and classify potential attacks, while also recognizing security vulnerabilities, thereby enabling timely alerts for network administrators. This study delves into the application of the Random Forest algorithm as the core technique for intelligent intrusion detection. The efficacy of the proposed approach was evaluated using the NSL-KDD dataset, a widely recognized benchmark in intrusion detection research. This dataset comprises 125,973 samples with 41 distinct features representing various network traffic characteristics. The Random Forest algorithm, known for its ensemble-based nature, constructs multiple decision trees during training and outputs the class that is the mode of the classes (classification) of the individual trees. This method enhances predictive accuracy and controls overfitting. Experimental results indicate that the use of this algorithm significantly improves the accuracy of intrusion detection, achieving a remarkable detection rate of 99.89%. These findings underscore the potential of Random Forest in developing intelligent and reliable IDS, offering a robust solution for real-world network security applications. The study also discusses the algorithm's performance in terms of precision, recall, and F1-score, highlighting its effectiveness in various attack scenarios. https://www.tmachineintelligence.ir/article_159732_82ac9e8ca6407115baece9053bf9252b.pdf