Intrusion detection and prevention systems are widely researched areas, rightly so being an integral part of network. As with all recent computing trends, Machine Learning and Deep Learning techniques have become extremely prevalent in intrusion detection and prediction systems security. The Intrusion detection system is used to detect and notify any malware activities and try to stop them. Soft computing techniques have the ability in learning data sets which is provided and it can also categories the packets or file coming through the network or any other source as normal and abnormal. Here, we will focus more on using Support Vector Machine (SVM) and Artificial Neural Network (ANN). In the proposed method, we are using SVM and ANN algorithms for the detection of malware; the data set is processed through SVM and ANN algorithms and compares their performances with respect to accuracy metrics. Since accuracy does not give a clear picture about how well classification algorithms perform, we have also measured and compared the performances of these two algorithms using AUC score. The AUC score is a value that ranges from 0 to 1 and closest to 1 will be considered as a better one. The results show that ANN can be implemented effectively for malware detection and is comparatively better than SVM.