Darknet Traffic Analysis and Classification Using Numerical AGM and Mean Shift Clustering Algorithm

The cyberspace continues to evolve more complex than ever anticipated, and same is the case with security dynamics there. As our dependence on cyberspace is increasing day-by-day, regular and systematic monitoring of cyberspace security has become very essential. A darknet is one such monitoring framework for deducing malicious activities and the attack patterns in the cyberspace. Darknet traffic is the spurious traffic observed in the empty address space, i.e., a set of globally valid Internet Protocol (IP) addresses which are not assigned to any hosts or devices. In an ideal secure network system, no traffic is expected to arrive on such a darknet IP space. However, in reality, noticeable amount of traffic is observed in this space primarily due to the Internet wide malicious activities, attacks and sometimes due to the network level misconfigurations. Analyzing such traffic and finding distinct attack patterns present in them can be a potential mechanism to infer the attack trends in the real network. In this paper, the existing Basic and Extended AGgregate and Mode (AGM) data formats for darknet traffic analysis is studied and an efficient 29-tuple Numerical AGM data format suitable for analyzing the source IP address validated TCP connections (three-way handshake) is proposed to find attack patterns in this traffic using Mean Shift clustering algorithm. Analyzing the patterns detected from the clusters results in providing the traces of various attacks such as Mirai bot, SQL attack, and brute force. Analyzing the source IP validated TCP, darknet traffic is a potential technique in Cyber security to find the attack trends in the network.