This section contains research articles that we are allowed to publish describing our technology and concepts:
Individual anomaly detection methods for monitoring computer network traffic have relatively high error rates. An agent-based trustmodeling system fuses anomaly data and progressively improves classification to achieve acceptable error rates.
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This article presents an empirical study of regret minimization procedure used in a distributed Intrusion Detection System (IDS) to independently adapt the self-contained components of the system without any explicit coordination. We show that the regret minimization methods can be used to build survivable distributed security systems that can only communicate using standard data-transfer protocols (NetFlow, selective traffic mirroring or alerts) and do not need to rely on explicit communication required by more elaborate coordination protocols
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With increasing amount of network traffic, sampling techniques have become widely employed allowing monitoring and analysis of high-speed network links. Despite of all benefits, sampling methods negatively influence the accuracy of anomaly detection techniques and other subsequent processing. In this paper, we present an adaptive, featureaware sampling technique that reduces the loss of information bounded with the sampling process, thus minimizing the decrease of anomaly detection efficiency.
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This paper presents an algorithm that is able to pro- gressively discover nodes of a Skype overlay P2P network. Most notably, super nodes in the network core.
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The paper presents security platform based on agents as an efficient and robust solution for high-performance intrusion detection system designed for deployment on highspeed network links.
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