Data-driven Approaches for Peer-to-Peer Botnet Detection and Forecasting
Peer-to-Peer(P2P) botnet is one of the major threats in network security for serving as the infrastructure that is responsible for various cybercrimes. Enterprises routinely collect terabytes of security-relevant data. First part of our work exploits such data to propose a novel large-scale P2P botnet detection that fuses big data behavioral analytics in conjunction with graph theoretical concepts.
It is important to organization that they have significant insights about targeted attack to understand future short and long term trend of ongoing P2P botnet attack. This helps to quantify attack impact like intensity and number of targeted machines. Second part of our work focused on using time series analysis to identify those features and thus provide the capability to recognize the current as well as future similar situations and hence appropriately respond to the threat.
Committee: Carl Chang (major professor), Morris Chang (major professor), Yong Guan, Simanta Mitra, and Wensheng Zhang