The Intrusion network (INetwork) connects systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of the Intrusion network. These intrusions may steal important information that causes economic and reputational damages. In order to identify harmful activity on target computers, a number of systems currently exist; yet occasionally an external user will produce malicious behavior and get unauthorized access to the victims’ computers. This behavior is referred to as nefarious actions or an intruder. In this paper, we have proposed a combined Machine learning approach to detect pirated software and malware-infected files across the Intrusion network. Machine Learning (ML) algorithms are applied in IDS in order to identify and classify security threats. Numerous machine learning and soft computing techniques are designed to detect the activities in real-time network log audit data. KKDDCUP99 and NLSKDD most utilized data sets to detect the Intruder on the benchmark data set. Two different techniques have been proposed a signature with detection and anomaly-based detection. The experimental analysis demonstrates SVM, Naïve Bayes, and ANN algorithms with various data sets and demonstrates system performance in the real-time network environment.