A Novel Permutational Sampling Technique for Cooperative Network Scanning Random IP address scanning is a seminal network reconnaissance technique in discovering machines by portscanners and for target or peer discovery by malwares. Among various random sampling techniques for scanning, cooperative or permutational scanning achieves the highest efficiency by allowing scanning agents to collaboratively ensure that every address in the target range is scanned only once, thus minimizing the overall scanning time and footprint (number of scans). Yet, no practical distributed mechanism for norepetition sampling has been proposed in the literature, and existing approaches only strive for minimizing the repetition. In this paper, by relying on a unique property of primitive roots of prime numbers, we propose a practical distributed permutational sampling method that enables a group of agents to cooperatively scan an IPv4 address space without even a single repetition and very low execution time. Through analytical modeling and simulation, we show that our approach significantly outperforms existing scanning techniques, in reducing the scanning time and especially the scanning footprint. We also show that our approach achieves high pseudorandomness (entropy) and robustness against bruteforce guessing attacks. We also discuss potential defensive countermeasures against this approach for both fast highrate and stealthy lowrate scanning.

TimeVarying Graph Patrolling Against Attackers with Locally Limited and Imperfect Observation Models The use of autonomous robots for surveillance is one of the most interesting applications of graphpatrolling algorithms. In recent years, considerable effort has been devoted to tackling the problem of efficiently computing effective patrolling strategies. One of the mainstream approaches is adversarial patrolling, where a model of a strategic attacker is explicitly taken into account. A common assumption made by these techniques is to consider a worstcase attacker, characterized by ubiquitous and perfect observation capabilities. Motivated by the domain of robotic applications, we instead consider a more realistic and limited attacker model capable of gathering noisy observations in a locally limited range of the environment. We assume that the modeled attacker follows a behavior induced by its observations. Thus, we devise a randomized patrolling strategy based on Markov chains that makes observations reveal very little information, while still maintaining a reasonable level of protection in the environment. Our experimental results obtained in simulation confirm timevariance as a practical approach for our objective.