Detection of Malware to Enhance the Network Accuracy using Ensemble based Classifier

Detection of malware is a complex process. Many developers face problem in detecting the malware. The Malware is program or software that damages the computer system. Malicious Software is “any code added, changed, or removed from a software system to intentionally cause harm or subvert the system’s intended function”. Malware is a type of intrusion in the computer network. Excellent technology exists for detecting known malicious executables. Software for virus detection has been quite successful, and programs such as McAfee Virus Scan and Norton Anti-Virus are ubiquitous. The behavior of malware is monitored and recorded. The KDDCUP 99 dataset is a standard dataset which is analyzed. This is used as training set to train the supervised classifiers. The classification model is trained using the training dataset. Different classification techniques like decision tree, naïve Bayes, k-nearest neighbour etc., are trained and evaluated for their accuracy, precision, recall and fmeasure. The most accurate classifier is selected for production. The new software data is fed to the selected classifier for detection of malware and classify it as malware or genuine software.