Automatic Detection of Social Engineering Attacks Using Dialog
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 6
Abstract
Abstract: Cyber attacker target the weakest part of security system which is increasingly the people who use and interact with a computer-based system is the easiest way for cyber attacker to attack the user. A separate research area is provided in order to protect computer assets. But by exploiting human vulnerabilities, an attacker can find a way for many computer-based defences. Phishing is the most common attack of social engineering attacks where someone pretend to be an authority figure or someone your trust to gain access to your login information. A robust and novel approach is needed to detect social engineering attacks and to send alert to the user while sharing confidential information to the unauthorized target. We proposed an approachwhich uses a dynamic Topic Blacklist (TBL) to verify the discussion topics of each line of text generated by the potential attacker. Using Natural language processing and machine learning, system can automatically detectsocial engineering attacks. Our approach is applicable to any attack vector since it depends only on the dialog text. System can recognize shortcut as well as incorrect grammar.
Authors and Affiliations
Pooja kale, Shital Kashiwant, Nisha Kamble, Subodh Awate Prof. Sonali Tidke
Keywords
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- EP ID EP90349
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