Development of cyber situation awareness model

Dauda Adenusi, B. K. Alese, B. Kuboye, A. Thompson
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引用次数: 2

Abstract

This study designed and simulated cyber situation awareness model for gaining experience of cyberspace condition. This was with a view to timely detecting anomalous activities and taking proactive decision safeguard the cyberspace. The situation awareness model was modelled using Artificial Intelligence (AI) technique. The cyber situation perception sub-model of the situation awareness model was modelled using Artificial Neural Networks (ANN). The comprehension and projection submodels of the situation awareness model were modelled using Rule-Based Reasoning (RBR) techniques. The cyber situation perception sub-model was simulated in MATLAB 7.0 using standard intrusion dataset of KDD'99. The cyber situation perception sub-model was evaluated for threats detection accuracy using precision, recall and overall accuracy metrics. The simulation result obtained for the performance metrics showed that the cyber-situation sub-model of the cybersituation model better with increase in number of training data records. The cyber situation model designed was able to meet its overall goal of assisting network administrators to gain experience of cyberspace condition. The model was capable of sensing the cyberspace condition, perform analysis based on the sensed condition and predicting the near future condition of the cyberspace.
网络态势感知模型的开发
本研究设计并模拟了网络态势感知模型,以获取网络空间状况的经验。这是为了及时发现异常活动,采取积极的决策,维护网络空间。态势感知模型采用人工智能(AI)技术建模。利用人工神经网络对态势感知模型中的网络态势感知子模型进行建模。利用基于规则的推理(RBR)技术对态势感知模型的理解子模型和投射子模型进行建模。利用KDD'99标准入侵数据集,在MATLAB 7.0中对网络态势感知子模型进行仿真。使用精度、召回率和总体准确性指标评估网络态势感知子模型的威胁检测准确性。对性能指标的仿真结果表明,随着训练数据记录数量的增加,网络态势模型的网络态势子模型性能更好。所设计的网络态势模型能够满足其协助网络管理员获得网络空间状况经验的总体目标。该模型具有感知网络空间状况、基于感知状况进行分析和预测网络空间近期状况的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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