Identifying Malicious Web Links and Their Attack Types in Social Networks
R. Hamsa Veni, A.Hariprasad Reddy, C.Kesavulu · Zenodo (CERN European Organization for Nuclear Research) · 2018
Malicious URLs are wide wont to mount numerous cyber attacks together with spamming, phishing and malware. Detection of malicious URLs and identification of threat varieties area unit important to thwart these attacks. Knowing the type of a threat permits estimation of severity of the attack and helps adopt a good step. Existing strategies usually notice malicious URLs of one attack kind. During this paper, we have a tendency to propose methodology using machine learning to notice malicious URLs of all the popular attack varieties and establish the character of attack a malicious address tries to launch. Our method uses a range of discriminative options together with matter properties, link structures, webpage contents, DNS information, and network traffic. Several of those options are novel and extremely effective.