Performance Evaluation of Machine Learning Algorithms on the Intrusion Detection System of Military Defence Operations

Intrusion detection system is used against harmful attacks also attacker's tools and strategies are always evolving. But implementing an authorized IDS system is equally a significant job. Several experiments were carried out and analyzed in this work in order to evaluate several machine learning and few deep learning classifiers in military defense environment (KDD dataset, NSL-KDD dataset) It was successful in computing numerous performance measures with the purpose of evaluate the chosen classifiers. The emphasis was on false negative and false positive performance indicators to strengthen the intrusion detection system's detection The executed trials revealed that the random forest and SVM has given markable results.