Software measure in cyber-attacks on production control system

Kawanaka, Takaaki; Matsumaru, Masanobu; Rokugawa, Shuichi

doi:10.1016/j.cie.2014.08.008

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Unnecessary patches are usually avoided on these nodes to prevent any unnecessary interruption in a production line. ICS nodes are also built using heterogeneous technologies and with a variety of operating systems; hence, the fixes are not released in time [30]. These nodes usually do not have direct network connectivity and are secured in demilitarised zones (DMZ).…”

Section: Managementioning

confidence: 99%

Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

2022

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The development of cyber-assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature.

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Section: Managementioning

confidence: 99%

Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

2022

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“…Cyber activities have been recognised as a destructive phenomenon in private and public enterprises (de Werra and Studer, 2017;Van Niekerk, 2016;Hills and Batchelor, 2015;Kawanaka et al, 2014;Ventre, 2013;Kim et al, 2011;Luo and Liao, 2009). Moreover, the major threat of cyberattacks, which have occurred in the last decade, has showed the vulnerabilities in cyber defence of the North Atlantic Treaty Organization (NATO), the European Union (EU), and the United Nations (UN).…”

Section: Introductionmentioning

confidence: 99%

Challenges and Vulnerabilities of Analysing Cybercrime Costs

Kuklytė¹

2017

EJOBSAT

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Recent studies have underlined a limited scope of research published with regard to the impact of cybercrimes, which is investigated by applying the scientific literature analysis and surveys. This paper focuses on in-depth research on cybercrime costs by analysing the information from selected online materials in order to reveal a research gap. To support the contributions of the research in the field, two methods, namely, literature review and statistical analysis were employed. The findings reveal that several interested parties such as independent IT companies, governmental and non-governmental institutions have conducted various surveys to identify the impact of cyberattacks. The main challenges and vulnerabilities of analysing cybercrime costs can be overcome by further investigations. KEY WORDScybercrime cots, cyber attack, OLS regression JEL CODESM21, K24

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“…Software vulnerability is one of the main causes of the information security. It is an important purpose of cyberattack and defense [2]. It is very important to discover and utilize the software vulnerabilities in the information security [3].…”

Section: Introductionmentioning

confidence: 99%

Software Vulnerability Prediction Models Based on Complex Network

Zhao¹,

Chen²,

Gao³

et al. 2018

dtcse

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Abstract. Recently, the software has been developed increasingly. It is more and more difficult to find software vulnerabilities. Therefore, this paper proposes a software vulnerability prediction model based on the complex network. This model regards the software defect evaluation as three classifications or regression sub-problems, including the defects in each class, software defect severity evaluation and software defect priority evaluation. This model uses machine learning method to train complex network feature, object-oriented feature and structure feature. The model can evaluate software defects on the hierarchy of class and assess software vulnerability effectively. An experiment on Hibernate is carried out, which selects random forest model as each sub-model. The experiment shows that the model is valid and accurate to evaluate vulnerability.

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Software measure in cyber-attacks on production control system

Cited by 6 publications

References 7 publications

Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

Challenges and Vulnerabilities of Analysing Cybercrime Costs

Software Vulnerability Prediction Models Based on Complex Network

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