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ThesisItem Open Access An integrated approach for cyber attack prediction using Honeynet and Socialnet data, applying improved association rule mining technique(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Agarwal, Bhavna; Samantaray, S.D.Cyber attacks are becoming more lethal in last couple of years, as it has become a parallel industry for targeting Government, Defence, Nuclear and other High-profile organisations. People usually commit cyber-crimes for demanding ransom, for fame or due to personal grudges. Lombroso theory for crime psychology uses certain traits of a person to identify whether that person is a criminal. Similarly understanding the Crime psychology of attackers help in mitigating the cyber attacks. Finding associations of Crime with spatial and temporal parameters such as locations, time, event and activity is still an open research area. In the thesis an Integrated approach for cyber attack prediction is proposed on the basis of the data acquired from the Honeynet and Socialnet. For implementation, local Honeynets namely CDAC CTMS and Pant Honeynet have been used which are deployed in Department of Computer Engineering at GBPUAT, Pantnagar. The Socialnet data is acquired using News Tracker API. The Honeynet provides us attack data in the form of activity logs while the Socialnet provides us events which correlate with the cyber attacks. The attack data and events obtained from Honeynet and Socialnet respectively are used to form an Augmented Transaction Database. Then Improved Association Rule Mining Technique is applied for producing Association Rules. These Association Rules help in predicting the occurrence of cyber-attack which would assist in providing better advisory and deployment of security measures.