Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps

Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps
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使用模糊认知图向入侵检测系统添加上下文信息

DOI:
10.1109/cogsima.2016.7497807
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发表时间:
2016
期刊:
--
影响因子:
--
通讯作者:
Aparicio-Navarro F
Aparicio-Navarro F
中科院分区:
--
文献类型:
--
作者:
Aparicio-Navarro F

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在过去的几年里,入侵检测系统(入侵检测系统)的效率有了很大的提高。然而,网络仍然是攻击的受害者。随着这些攻击的复杂性不断增加,需要开发新的、更健壮的检测机制。下一代入侵检测系统的设计应该结合推理引擎,包括关于网络的上下文信息、来自网络用户的认知信息和态势感知,以改进其检测结果。在本文中,我们建议将模糊认知图(FCM)与入侵检测系统结合使用,以将上下文信息纳入检测过程。我们已经评估了使用FCM来调整在数据融合过程之前定义的基本概率分配(BPA)值,这对我们开发的入侵检测系统至关重要。实验结果表明,FCMS能够在不影响正确检测次数的前提下,减少误报次数,从而提高入侵检测系统的效率。
In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information from the network users and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections.
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