In this paper we describe architecture and implementation of a Probabilistic Agent-Based Intrusion Detection (PAID) system. The PAID system has a cooperative agent architecture. Autonomous agents can perform specific intrusion detection tasks (e.g., identify IP-spoofing attacks) and also collaborate with other agents. The main contributions of our work are the following: our model allows agents to share their beliefs, i.e., the probability distribution of an event occurrence. Agents are capable to perform soft-evidential update, thus providing a continuous scale for intrusion detection. We propose methods for modelling errors and resolving conflicts among beliefs. Finally, we have implemented a proofof-concept prototype of PAID. ª

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Abstract. Enterprise Rights Management (ERM) systems aim to protect disseminated data even after it has been sent to remote locations. Existing systems are based on common components, have similar functionalities and often have two shortcomings: a centralised architecture and a lack of concern for the trust and privacy of data recipients. To access the data, recipients must present their credentials to a policy evaluation authority, which they cannot choose and may not trust. Furthermore, recipients may be unable to access the data if their connection is intermittent or if they are off-line. To address these limitations, we propose PAES: a Policy-based Authority Evaluation Scheme, which combines data protection with a distributed policy evaluation protocol. The result allows us to implement the sticky policies paradigm in combination with trust management techniques. This permits distributing policy evaluation over a flexible set of authorities, simultaneously increasing the resilience of policy enforcement.