AN EXPERT SYSTEM FOR DETECTION AND PREVENTION OF SOCIAL ENGINEERING ATTACKS USING MACHINE LEARNING ALGORITHM
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Abstract
Social engineering is the ART OF CONVENCING PEOPLE TO REVEAL THERE INFORMATION .social engineers depend on the fact that people are unaware of there valuable information and not care about there personal information also careless about protecting that information.one of the type of social engineering is insider attack where insider intentionally violating rule of the organization where he is working this sort of attack generally done by third party or terminated employee for there financial gain or revenge or sometime for future competitors reason can be anything like mentioned
In social engineering it uses other tactics to get people's attention to achieve sensitive information for a variety of purposes. Theft of sensitive information is a form of social engineering attack. Theft of sensitive information is a criminal act of obtaining personal information by sending fake emails with fake websites and fake weblinks on web pages. The purpose of this study is to develop a system that will find URLs that steal sensitive information from a web page and separate the URL whether it is a valid or illegal URL using a machine learning algorithm.
The Prevailing conditions during COVID-19, more number of peoples started moving to Internet medium for their everyday activities. This have increased in the presence of people on the Internet is almost never preceded by education about cyber security and various types of attacks. Social engineering attacks are a group of sophisticated cyber security attack that exploit the innate huma nature to breach securing systems and thus have some of the highest rate of success. The research is to explore into the particulars of how COVID-19 pandemic has set the stage for an increase in social engineering attack, the possible method of identifying the attacks technologies and mitigate them.
Key Words: Phishing URL, Legitimate URL, Machine Learning, Logistic Regression, Prediction.
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Article Details
Phishing URL, Machine Learning
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