Programming and Deploying Java Mobile Agents Aglets
Programming and Deploying Java Mobile Agents Aglets
Classification of malicious host threats in mobile agent computing
SAICSIT '02 Proceedings of the 2002 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology
ACM Transactions on Internet Technology (TOIT)
Boosting-Based Learning Agents for Experience Classification
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Determining confidence when integrating contributions from multiple agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Systematic Survey of Self-Protecting Software Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section on Best Papers from SEAMS 2012
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The use of mobile agents to support the development of practical applications is limited primarily by the risks to which hosts in the system are subject to. This article introduces a distributed and adaptive security-monitoring framework to decrease such potential threats. The proposed framework is based on a modified version of the popular Boosting algorithm to classify malicious agents based on their execution patterns on current and prior hosts. Having implemented the framework for the Aglet platform, we herein present the results of our experiments showcasing the detection of agent entities in the system with intention deviating from that of their well-behaved counterparts.