Automatic Method to Predict and Classify Cyber Hacking Breaches using Machine Learning

The fast propagation of computer networks has changed the viewpoint of network security. Easy accessibility conditions cause computer networks to be susceptible against several threats from hackers. Threats to networks are numerous and potentially devastating. Up to the moment, researchers have developed Malware Detection Systems (MDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied. Many of the technologies proposed are complementary to each other, since for different kinds of environments some approaches perform better than others. This project presents a new Malware detection system that is then used to survey and classify them. The taxonomy consists of the detection principle, and second of certain operational aspects of the Malware detection system. In our project we have used algorithms like Random Forest (RF) as existing and Support Vector Machine (SVM) as proposed systems. From the results it is proved that the proposed SVM will work better than existing RF. All are measured in terms of accuracy