Multiagent patrolling in adversarial domains has been widely studied in recent years. However, little attention has been paid to cooperation issues between patrolling agents. Moreover, most existing works focus on one-shot attacks and assume full rationality of the adversaries. Nonetheless, when patrolling frontiers, detecting illegal fishing or poaching; security forces face several adversaries with limited observability and rationality, that perform multiple illegal actions spread in time and space. In this paper, we develop a cooperative approach to improve defenders efficiency in such settings. We propose a new formalization of multiagent patrolling problems allowing for effective cooperation between the defenders. Our work accounts for uncertainty on action outcomes and partial observability of the system. Unlike existing security games, a generic model of the opponents is considered thus handling limited observability and bounded rationality of the adversaries. We then describe a learning mechanism allowing the defenders to take advantage of their observations about the adversaries and to compute cooperative patrolling strategies consequently.