DarkSim: A similarity-based time-series analytic framework for darknet traffic
Max Gao, Ricky K. P. Mok, Esteban Carisimo, Eric Li, Shubham Kulkarni, kc claffy · 2024
Network Telescopes, often referred to as darknets, capture unsolicited traffic directed toward advertised but unused IP spaces, enabling researchers and operators to monitor malicious, Internet-wide network phenomena such as vulnerability scanning, botnet propagation, and DoS backscatter. Detecting these events, however, has become increasingly challenging due to the growing traffic volumes that telescopes receive. To address this, we introduce DarkSim, a novel analytic framework that utilizes Dynamic Time Warping to measure similarities within the high-dimensional time series of network traffic. DarkSim combines traditional raw packet processing with statistical approaches, identifying traffic anomalies while enabling rapid time-to-insight. We evaluate our framework against DarkGLASSO, an existing method based on the Graphical LASSO algorithm, using data from the UCSD Network Telescope. Based on our manually classified detections, DarkSim showcased perfect precision and an overlap of up to 91% of DarkGLASSO's detections in contrast to DarkGLASSO's maximum of 73.3% precision and detection overlap of 37.5% with the former. We further demonstrate DarkSim's capability to detect two real-world events in our case studies: (1) an increase in scanning activities surrounding CVE public disclosures, and (2) shifts in country- and network-level scanning patterns that indicate aggressive scanning. DarkSim provides a detailed and interpretable analysis framework for time-series anomalies, representing a new contribution to network security analytics.