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Robust detection of malicious behavior in distributed wireless networks.

分布式无线网络中恶意行为的稳健检测。

基本信息

批准号：
234874366
负责人：
Professor Dr.-Ing. Holger Boche
金额：
--
依托单位：
Lehrstuhl für Theoretische Informationstechnik (LTI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/234874366?language=en
关键词：
Robust detection malicious behavior distributed

项目摘要

This project aims at developing a better understanding of security architectures for wireless networks, with particular focus on wireless sensor networks (e.g. in industrial environments for detecting malfunctions in production processes) and cognitive radio networks, taking into account the unique features and limitation of these systems. The core premise of the proposal is that efficient on-line malware detection algorithms must account for possibly unreliable or inaccurate information due to transmission errors or faulty devices, and optimize the utilization of scarce available wireless resources, including an appropriate prioritization of protected network elements. The first step towards this goal is the development of a general statistical framework, which shall encompass a number of different malfunctions, attacks, and anomalous behaviors in wireless networks. This model will serve as a starting point and common background for the application of a variety of statistical inference and machine learning techniques. The analysis will be then complemented by considering the anomaly detection problem jointly with the resource allocation problem, in an effort to conjugate security with system-level network optimization. Thus, statistical detection theory will be combined with optimization theory, game theory and information theory. Finally, the high variability of data provided by individual sensor nodes naturally calls for distributed solutions, especially in large-scale networks. As some of the devices actively involved in the collaborative decision process might be malfunctioning or under the control of malicious users, the project will investigate incentive mechanisms and selective cooperation schemes based, for instance, on estimated reputation values assigned to each node.

该项目旨在更好地了解无线网络的安全架构，特别关注无线传感器网络（例如，在工业环境中用于检测生产过程中的故障）和认知无线电网络，同时考虑到这些系统的独特功能和局限性。该提案的核心前提是，有效的在线恶意软件检测算法必须考虑到由于传输错误或故障设备而导致的可能不可靠或不准确的信息，并优化稀缺的可用无线资源的利用，包括对受保护的网络元素进行适当的优先级排序。实现这一目标的第一步是开发一个通用的统计框架，其中应包括无线网络中的一些不同的故障，攻击和异常行为。该模型将作为各种统计推断和机器学习技术应用的起点和共同背景。然后，通过考虑异常检测问题和资源分配问题来补充分析，以便将安全性与系统级网络优化结合起来。因此，统计检测理论将与最优化理论、博弈论和信息论相结合。最后，单个传感器节点提供的数据的高度可变性自然要求分布式解决方案，特别是在大规模网络中。由于积极参与协作决策过程的一些设备可能出现故障或受到恶意用户的控制，该项目将调查激励机制和选择性合作方案，例如，基于分配给每个节点的估计声誉值。