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 detection system model for this issue. The designed system detects the worm malware that depends on the information of the dataset that is taken from Kaspersky company 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. The results of the experiments were 95% of worm detection accuracy and 98% of detection rate with 21% false positives, which makes it more accurate and effective to detect the worm malware by using the proposed dataset for this work.
Network Security, Worm Detection, Malware, Naïve Bayesian, Data Mining.