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Analytics and Tracking


Analytics and Tracking

Drive-by-download attacks exploit vulnerabilities in Web systems, and target systems unnoticeably download malware, through web system visit which access the user system. This attack ranges from malware attack to virus and it is a major challenge to traditional detection tools such as Intrusion Detection System and DNS Detection systems etc. Artificial Intelligent and Machine Learning plays an important role in cyber security, malware attack detection, as it has being used for attack detection and injection of attacks. Cyber attack like Drive-by Download operates in a dynamic manner, as they execute when arrive at the target. This type of attack is injected into Web systems which play a major role in conveying malware or malicious code and triggers its infection when such sites are visited [1]. Based on the dynamic nature of drive-by attack, I propose a hybrid method to analyze and detect malware attack using denoising autoencoder and deep belief network model. This method focuses on studying and analyzing the malicious web links and identifying attack vectors.