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      Mobile Network Anomaly Detection and Mitigation: The NEMESYS Approach

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          Abstract

          Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware.

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          Random Neural Networks with Negative and Positive Signals and Product Form Solution

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            Learning in the Recurrent Random Neural Network

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              Steps toward self-aware networks

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                Author and article information

                Journal
                1305.4210

                Security & Cryptology,Networking & Internet architecture
                Security & Cryptology, Networking & Internet architecture

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