Agents that reduce work and information overload
Communications of the ACM
A calculus for mass assignments in evidential reasoning
Advances in the Dempster-Shafer theory of evidence
Testing and evaluating computer intrusion detection systems
Communications of the ACM
The base-rate fallacy and its implications for the difficulty of intrusion detection
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
A fuzzy model of reputation in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
An Intrusion Detection System for Aglets
MA '02 Proceedings of the 6th International Conference on Mobile Agents
An Architecture for Intrusion Detection Using Autonomous Agents
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Multi-agent framework in visual sensor networks
EURASIP Journal on Applied Signal Processing
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
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This paper proposes a system of agents that make predictions over the presence of intrusions. Some of the agents act as predictors implementing a given Intrusion Detection model, sniffing out the same traffic. An assessment agent weights the forecasts of such predictor agents, giving a final binary conclusion using a probabilistic model. These weights are continuously adapted according to the previous performance of each predictor agent. Other agent establishes if the prediction from the assessor agent was right or not, sending him back the results. This process is continually repeated and runs without human interaction. The effectiveness of our proposal is measured with the usual method applied in Intrusion Detection domain: Receiver Operating Characteristic curves (detection rate versus false alarm rate). Results of the adaptive agents applied to intrusion detection improve ROC curves as it is shown in this paper.