AFMT: Maintaining the safety-security of industrial control systems

Kumar, Rajesh; Narra, Bhavesh; Kela, Rohan; Singh, Siddhant

doi:10.1016/j.compind.2021.103584

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…These connected devices enable interaction between the physical and digital worlds. They include computing, storage and communication functions that enable them to manage objects in the physical world [1,2] and provide services that deliver significant benefits in numerous areas such as healthcare, energy supply, transportation, industrial automation and smart homes [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

QISS: Quantum-Enhanced Sustainable Security Incident Handling in the IoT

Blanco,

Santos-Olmo,

Sánchez

2024

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As the Internet of Things (IoT) becomes more integral across diverse sectors, including healthcare, energy provision and industrial automation, the exposure to cyber vulnerabilities and potential attacks increases accordingly. Facing these challenges, the essential function of an Information Security Management System (ISMS) in safeguarding vital information assets comes to the fore. Within this framework, risk management is key, tasked with the responsibility of adequately restoring the system in the event of a cybersecurity incident and evaluating potential response options. To achieve this, the ISMS must evaluate what is the best response. The time to implement a course of action must be considered, as the period required to restore the ISMS is a crucial factor. However, in an environmentally conscious world, the sustainability dimension should also be considered to choose more sustainable responses. This paper marks a notable advancement in the fields of risk management and incident response, integrating security measures with the wider goals of sustainability and corporate responsibility. It introduces a strategy for handling cybersecurity incidents that considers both the response time and sustainability. This approach provides the flexibility to prioritize either the response time, sustainability or a balanced mix of both, according to specific preferences, and subsequently identifies the most suitable actions to re-secure the system. Employing a quantum methodology, it guarantees reliable and consistent response times, independent of the incident volume. The practical application of this novel method through our framework, MARISMA, is demonstrated in real-world scenarios, underscoring its efficacy and significance in the contemporary landscape of risk management.

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Section: Introductionmentioning

confidence: 99%

QISS: Quantum-Enhanced Sustainable Security Incident Handling in the IoT

Blanco,

Santos-Olmo,

Sánchez

2024

Information

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“…In the category of national information security, the security of the industrial control system associated with the national infrastructure occupies an extremely important position 1 . At present, the security of the industrial control system in the Internet environment is an important concern.…”

Section: Introductionmentioning

confidence: 99%

Malware behavior detection method based on reinforcement learning

Cui

Leng

Wang

et al. 2023

International Conference on Computer Application and Information Security (ICCAIS 2022)

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Malware in the network environment is a serious threat to the security of industrial control systems. With the gradual increase of malware variants, it brings great challenges to the detection and security protection of industrial control system malware. The existing detection methods have limitations such as low intelligence in adaptive detection and recognition. In response to this problem, this paper designs a detection application method framework by combining the use of reinforcement learning, an advanced machine learning algorithm, around the malware objects that threaten the network security of industrial control systems. In the implementation process, according to the actual needs of malware behavior detection, fully combined with intelligent features such as sequential decision-making and dynamic feedback learning of reinforcement learning, the key application modules such as feature extraction network, policy network and classification network are discussed and designed in detail. The application experiments based on the actual malware test data set verify the effectiveness of the method in this paper, which can provide an intelligent decision-making aid for general malware behavior detection.

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