An Analysis of Various Social Engineering Attack in Social Network using Machine Learning Algorithm

"Social engineering explains how one can use the human mind for capturing useful information about organizations or individuals." With tremendous growth of internet, attack cases are increasing each day along with the modern attack method, and It targets emotional parts of human to gain access to controlled area or achieve sensitive information for various purposes. Since there is neither hardware or software available to protect an enterprise or individual against social engineering, it is essential that good practices be implemented . The overall purpose of this research is to highlight the different social engineering attacks and how they can prevent in social network because Social engineering is one of the biggest problems in social network, a concern the privacy and security. And we have another problem on social networks, that it is difficult for users to judge if a friend request is trustworthy or not, and always users of online social networks tend to exhibit a higher degree of trust in friend requests and messages sent by other users, Social engineering lead to increase the Incorporate threats, fear and a sense of urgency in an attempt to manipulate the user into responding quickly. For this purpose, we will use J48 algorithm to implement a detection algorithm for social engineering attacks in URL links. Thereafter, this project is using a set of data then analysis it using the Weka tool, to defend against these attack J48 approach is very simple and effective in decreasing the false alarm ratio and improving the intrusion detection accuracy. The potential estimate of J48 algorithm is to help in an effective detection of probable attacks which could jeopardies the social engineering confidentiality, also this proposed technique could classify the data as either normal or abnormal. This project presents the algorithm, that will help create a safer and more reliable computing environment around the world for our next generation.