Network Intrusion Detection System based on Machine Learning algorithm using UNSW-NB15 Dataset

Network play important role in real life and cyber security has become a vital in reaearch.An intrusion detection system (IDS)which is important in Network Intrusion Detectyion System(NIDS). In existing NIDS still face challeneges in improving detection accuracy,redusing the false alarm rate it used for reducing the false alarm rate and also detection unknown attacks. To solve this problem,researcher should have focused on developing IDSs using machine learning methods.ML easily discover normal and abnormal data with high accuracy.Machine Learning performance is remarkable. Intrusion Detection or malicious node detection (IDS) show a important role in safeguarding computer networks system from security threats. However, traditional IDS methods often struggle with high false positive rates and limited detection accuracy. This paper presents an advanced IDS that harnesses the power of a multi-class support vector machine (SVM) and integrates random forest technique for feature selection, specifically targeting the UNSW-NB15 dataset. The motivation behind this research is to enhance IDS performance by leveraging robust machine learning techniques. The research problem centers on improving the accuracy and efficiency of detecting various network attacks while reducing false positives. Our approach involves meticulously preprocessing network traffic data to extract relevant features, utilizing random forest to identify the most informative ones, and then employing these features to train a multi-class SVM classifier. This classifier effectively categorizes network traffic into different attack types. The results demonstrate a significant improvement in prediction accuracy and a decrease in rate of false positives, thereby enhancing the overall effectiveness of IDS in protecting computer networks against security threats.