AADS:开发认知无线电网络有害攻击的AGENT辅助防御系统

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Communication Networks and Distributed Systems Pub Date : 2019-03-27 DOI:10.1504/IJCNDS.2019.10013306
Natasha Saini, Nitin Pandey
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引用次数: 0

摘要

认知无线电网络(CRN)是一种新兴技术,它提供了适应无线设备工作参数的能力,以克服频谱稀缺问题。本文主要研究了CRN上的三种主要攻击,即主用户仿真攻击(PUE)、频谱感知数据伪造攻击(SSDF)和窃听攻击。为了克服攻击,本文设计了一种有效的agent辅助CRN防御系统。首先根据PBS的通信范围将网络划分为四个不同的组。为了减轻SSDF攻击,将恶意节点与网络隔离。该决策是由预测节点未来活动的三状态马尔可夫链模型(MCM)做出的。网络中的通信采用Diffie-Hellman加密(HAES-DHE)混合提前加密算法进行保护,以减轻窃听者的攻击。实验结果表明,该算法在检测概率、未检测概率、攻击强度估计、诚实节点平均估计和吞吐量等方面都有较大的改进。
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AADS: DEVELOPING AGENT ASSISTED DEFENSE SYSTEM AGAINST HARMFUL ATTACKS IN COGNITIVE RADIO NETWORK
Cognitive radio network (CRN) is an emerging technology that provides ability of adapting operating parameters to wireless devices in order to overcome spectrum scarcity problems. The paper mainly focused on three major attacks held on CRN such as, primary user emulation (PUE), spectrum sensing data falsification (SSDF), and eavesdropper attacker. To overcome attacks, this paper designs an effective agent assisted defence system in CRN. Initially network is partitioned into four different groups based on communication range of PBS. To mitigate SSDF attack the malicious node is isolated from the network. This decision is made by tri-state Markov chain model (MCM) which predicts future activity of the node. Communication in the network is protected by hybrid advance encryption with Diffie-Hellman encryption (HAES-DHE) algorithm to alleviate eavesdropper attack. Experimental result shows promising improvements in probability of detection, probability of miss detection, estimation of attack strength, average estimate of honest nodes and throughput.
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来源期刊
CiteScore
2.50
自引率
46.20%
发文量
57
期刊介绍: IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.
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