检测工业互联网恶意攻击行为的组合优化分析方法

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2023-12-15 DOI:10.1145/3637554
Kejing Zhao, Zhiyong Zhang, K. Choo, Zhongya Zhang, Tiantian Zhang
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引用次数: 0

摘要

工业互联网在关键基础设施领域发挥着重要作用,是各种安全威胁和风险的目标。现有的许多攻击检测方法存在功能冗余、过度拟合和效率低等局限性。设计了一种组合优化方法拉格朗日乘法器来优化底层特征筛选算法。优化后的特征组合与随机森林和 XG-Boost 筛选特征相融合,提高了攻击特征分析的准确性和效率。我们使用 UNSW-NB15 和天然气管道数据集评估了所提方法的性能。结果表明,与攻击行为相关的不同特征的影响度可使二元分类攻击检测的准确度提高到 0.93,攻击检测时间缩短了 6.96 倍。多分类攻击检测的总体准确率也提高了 0.11。我们还观察到,攻击行为分析的九个关键特征对于分析和检测针对系统的一般攻击至关重要,通过关注这些特征,有可能提高实时关键工业系统安全的有效性和效率。本文使用 CICDDoS2019 数据集和 CICIDS2018 数据集来证明该泛化方法。实验结果表明,所提出的方法具有良好的泛化能力,可以扩展到同类型的工业异常数据集。
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A Combinatorial Optimization Analysis Method for Detecting Malicious Industrial Internet Attack Behaviors
Industrial Internet plays an important role in key critical infrastructure sectors and is the target of different security threats and risks. There are limitations in many existing attack detection approaches, such as function redundancy, overfitting and low efficiency. A combinatorial optimization method Lagrange multiplier is designed to optimize the underlying feature screening algorithm. The optimized feature combination is fused with random forest and XG-Boost selected features to improve the accuracy and efficiency of attack feature analysis. Using both the UNSW-NB15 and Natural gas pipeline datasets, we evaluate the performance of the proposed method. It is observed that the influence degrees of the different features associated with the attack behavior can result in the binary classification attack detection increases to 0.93, and the attack detection time reduces by 6.96 times. The overall accuracy of multi-classification attack detection is also observed to improve by 0.11. We also observe that nine key features of attack behavior analysis are essential to the analysis and detection of general attacks targeting the system, and by focusing on these features one could potentially improve the effectiveness and efficiency of real-time critical industrial system security. In this paper, CICDDoS2019 dataset and CICIDS2018 dataset are used to prove the generalization. The experimental results show that the proposed method has good generalization and can be extended to the same type of industrial anomaly data sets.
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来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
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