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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 and enabling rapid time-to-insight. We evaluate our framework against DarkGLASSO, an existing method based on the GraphicalLASSO 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. 

A swarm of unmanned aerial vehicles (UAVs) can be used for many applications, including disaster relief, search and rescue, and establishing communication networks, due to its mobility, scalability, and robustness to failure. However, a UAV swarm’s performance is typically limited by each agent’s stored energy. Recent works have considered the usage of thermals, or vertical updrafts of warm air, to address this issue. One challenge lies in a swarm of UAVs detecting and taking advantage of these thermals. Inspired by hawks, a swarm could take advantage of thermals better than individuals due to the swarm’s distributed sensing abilities. To determine which emergent behaviors increase survival time, simulation software was created to test the behavioral models of UAV gliders around thermals. For simplicity and robustness, agents operate with limited information about other agents. The UAVs’ motion was implemented as a Boids model, replicating the behavior of flocking birds through cohesion, separation, and alignment forces. Agents equipped with a modified behavioral model exhibit dynamic flocking behavior, including relative ascension-based cohesion and relative height-based separation and alignment. The simulation results show the agents flocking to thermals and improving swarm survival. These findings present a promising method to extend the flight time of autonomous UAV swarms.