A Layered Intrusion Detection System for Critical Infrastructure Using Machine Learning

Begli, Mohammadreza; Derakhshan, Farnaz; Karimipour, Hadis

doi:10.1109/sege.2019.8859950

Cited by 54 publications

(22 citation statements)

References 18 publications

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“… Begli, Derakhshan & Karimipour (2019) designed a security framework for smart healthcare system, which can be specifically useful for protecting wireless sensor network against unauthorized access and network attacks. In their work, a multiagent based layered architecture was first defined.…”

Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…The critical infrastructure is any physical infrastructure, such as a power system, healthcare [25], or gas pipeline, that is essential to support our daily lives [26]. Hence, disturbance to these systems has a huge impact on society, the economy, and the environment [27].…”

Section: Critical Infrastructure Experimental Frameworkmentioning

confidence: 99%

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

et al. 2021

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Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.

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“…Intrusion and Malware Detection: Herein, the main achieved results on this topic that were reported in literature are given followed by a summarized comparison of the studies, as shown in Table 10. Begli et al (Begli et al 2019) designed a security framework for smart healthcare system, which can be specifically useful for protecting wireless sensor network against unauthorized access and network attacks. In their work, a multiagent based layered architecture was first defined.…”

Section: Tablementioning

confidence: 99%

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

2021

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Background . The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.Methodology. This paper is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 to 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis were conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results.It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

show abstract

A Layered Intrusion Detection System for Critical Infrastructure Using Machine Learning

Cited by 54 publications

References 18 publications

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

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