Architectural Tactics for Big Data Cybersecurity Analytics Systems: A Review

Ullah, Faheem; Babar, Muhammad Ali

doi:10.1016/j.jss.2019.01.051

Cited by 64 publications

(24 citation statements)

References 80 publications

(89 reference statements)

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“…然而基于大数据技术对安全数据进行分析也面临诸多挑战. Ullah 和 Babar [337] 从系统架构视角总结了大数据安全分析系统的研究现状, 提出互操作性、可修改性、适应性、通用性、隐蔽性和隐私保护这些重要属性在当前研究中缺乏明确的体系架构支持; 不同架构策略之间的权衡和依赖性需要更深入的探索; 学术界和工业界普遍缺乏有效协作以支持分析系统的建立; 不同大数据处理框架如 Hadoop, Spark 和 Storm 在大数据监管与态势分析中的性能需要进行更多的比较分析. 此外, 监管数据采集的全面性与可信性、监管数据的可视化能力等也是影响基于大数据技术的安全监管系统效能的重要因素, 相关研究有待进一步加强 [318,338] .…”

Section: 基于大数据技术的服务与平台安全监管unclassified

Research progress on big data security technology

Chen¹,

Gao²,

Huilin³

et al. 2020

Sci. Sin.-Inf.

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Independence and controllability of big data security Chinese Science Bulletin 60, 427 (2015); Research progress in the complexity theory and algorithms of big-data computation SCIENTIA SINICA Informationis 46, 1255 (2016); Real-time intelligent big data processing: technology, platform, and applications SCIENCE CHINA Information Sciences 62, 082101 (2019); Research on theory and key technology of trusted computing platform security testing and evaluation SCIENCE CHINA Information Sciences 53, 434 (2010); Earth observation big data for climate change research

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Section: 基于大数据技术的服务与平台安全监管unclassified

Research progress on big data security technology

Chen¹,

Gao²,

Huilin³

et al. 2020

Sci. Sin.-Inf.

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“…Case studies on the analysis of multi-source cyber-attacks present different ways of addressing this problem, and various methods have been introduced in the literature to protect the network systems [9], [23], [28], [31]. In this paper, we apply an incremental piecewise linear classification algorithm to identify cyber-attacks and their sources.…”

Section: Enterprise Cyber Security Data Sourcesmentioning

confidence: 99%

Multi-Source Cyber-Attacks Detection using Machine Learning

Taheri

Gondal

Bagirov

et al. 2019

2019 IEEE International Conference on Industrial Technology (ICIT)

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The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.

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“…Cyber security is set of tools, practice and guidelines to protect networked elements from attacks and unauthorized access. The aim is to avoid damages and bring safety and privacy to the hardware, software and data [2]. Collaborative efforts of cybersecurity professionals and researchers lead to designing a variety of cyber defense systems for the purpose of maintaining confidentiality, integrity and availability (CIA) of information in a cyber-environment [3].…”

Section: B Cyber Securitymentioning

confidence: 99%

Machine Learning Based Network Anomaly Detection

Kazim*,

Dr. Doreswamy

2019

IJRTE

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Network Anomaly Detection Systems (NADSs) play prominent role in network security. Due to dynamic change of malware in network traffic data, traditional tools and techniques are failing to protect networks from attack penetration. In this paper we propose a two-phase model to detect and categorize anomalies. First, we selected Random Forest based on the highest accuracy-score out of eleven commonly used algorithms tested with the same set of data. The RF is used to detect anomalies and generate an extra feature named “attack-or-not”. Secondly we fed Neural Network with the data having “attack-or-not” feature to differentiate attack categories, which will help treating each type accordingly. The model performance was good, it scored 0.99 for both Precision and Recall in anomaly detection phase and 0.93 for Precision and 0.88 for Recall in attack categorization phase. We used UNSW-NB15 data set in our study.

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Architectural Tactics for Big Data Cybersecurity Analytics Systems: A Review

Cited by 64 publications

References 80 publications

Research progress on big data security technology

Research progress on big data security technology

Multi-Source Cyber-Attacks Detection using Machine Learning

Machine Learning Based Network Anomaly Detection

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