Malware Classification using Machine Learning Algorithms and Tools

The explosive growth of malware variants poses a major threat to information security. Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. According to numerous increasing of worm malware in the networks nowadays, it became a serious danger that threatens our computers. Networks attackers did these attacks by designing the worms. A designed system model is needed to defy these threats, prevent it from multiplying and spreading through the network, and harm our computers. In this paper, we designed a classification on system model for this issue. The designed system detects the worm malware that depends on the information of the dataset that is taken from website, the system will receive the input package and then analyze it, the Naïve Bayesian classification technique will start to work and begin to classify the package, by using the data mining Naïve Bayesian classification technique, the system worked fast and gained great results in detecting the worm. By applying the Naïve Bayesian classification technique using its probability mathematical equations for both threat data and benign data, the technique will detect the malware and classify data whether it was threat or benign.