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Section: Computer Science and Engineering
Tutor: AMIGONI FRANCESCO Major Research topic
:Patrolling Adversarial Environments Exploiting an Alarm System
Advisor: GATTI NICOLAAbstract:
Physical security is one of the most important challenges of our times. Due to the terrible events happened in the last decades, e.g., on September 11, 2001, or nowadays in several European countries, new techniques and methods are being developed to face new threats and dangers. On the other side, security means also saving lives and helping people, e.g., detecting desperate migrants that are moving across the Mediterranean Sea and rescuing them.
Algorithmic Game Theory is a fundamental tool in these settings, giving us the possibility to scientifically investigate them, modeling the interaction between law enforcers and terrorists or criminals as a mathematical problem, and designing suitable algorithms to deal with these threats.
Often, in large environments or infrastructures, a constant surveillance of every area is not affordable: to cope with this issue, a common countermeasure is the usage of cheap but wide-ranged sensors, able to detect suspicious events that occur in large areas, supporting patrollers to improve the effectiveness of their strategies.
This is a two-phase approach: first, there is a surveillance phase, by means of sensors or drones to detect the intrusion and, only when an attack is actually detected, the patrollers rush towards the threat to block it. Given this scenario, how would you exploit the information that something is happening in some areas if you do not know the exact position of the attack? How would you coordinate multiple guards to catch the attacker? And how would you react if, once started moving towards the attack location, another alarm is triggered, telling you that multiple attacks are ongoing?
To tackle these problems, we propose the first Security Game model with the presence of an alarm system able to trigger alarm signals, which carry the information about the set of targets that can be under attack and it is described by the probability of being generated when each target is attacked. We investigate two main research lines.
On one side, we study the uncertainties that may affect a real-world alarm system: spatial uncertainty, i.e., the alarm system is uncertain about the exact target under attack. In other words, when an attack is occurring, a signal is sent to the Defender, saying that something bad is happening in an area but without specifying the exact location. We show that without false positives and missed detections, the best patrolling strategy reduces to stay in a place, wait for a signal, and respond to it at best. Then, we introduce a significant positive missed detection rate, i.e., no alarm signal is generated even though an attack is occurring. In particular, we show that standing still and waiting for a signal is no more the best response, while movements among the areas of the environment to protect should be considered.
Moreover, we introduce the notion of uncertainty also in the type of attacker we are playing against. Specifically, we tackle the problem of facing an unknown adversary, whose profile is just known to be in a list of possible profiles she can assume. The problem is completely different, requiring to identify the Attacker we are facing to exploit such information in the future and be able to prevent her from performing other attacks.
The other direction we investigate is about the dimension of the problem, i.e., the number of resources both the Attacker and the Defender can control. Thus, first we tackle the problem of finding the minimum number of resources assuring non-null protection to every target, which is of high relevance in practice due to resource costs and to the need for assuring a minimum level of protection to each target. Then, we study how the Defender should move such resources, taking into account also the level of coordination among such defensive resources, e.g., they cannot communicate because of a radio silence constraint since they are operating on an undercover mission.
On the other side, we investigate the opportunities the Attacker can take when she is allowed to perform multiple attacks, simultaneously or sequentially. Then, since the computation of the equilibrium strategies requires the common knowledge about the number of Attacker's resources but this information is unlikely to be common, we assume that the Defender makes a guess about the number of resources and plays her best strategy accordingly, and we evaluate the worst-case inefficiency of this strategy when the Attacker has a different number of resources.