CRNIMay 18, 2013

Mobile Network Anomaly Detection and Mitigation: The NEMESYS Approach

arXiv:1305.4210v149 citations
Originality Synthesis-oriented
AI Analysis

This addresses security issues for mobile network operators and users, but it appears incremental as it builds on existing anomaly detection techniques.

The paper tackles the growing threat of mobile malware and network attacks by proposing a network-based security solution that integrates analytical modeling, simulation, learning, and data analysis to detect and mitigate anomalies, as part of the EU FP7 NEMESYS project.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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