Towards an Automated Exploration of Secure IoT/CPS Design-Variants

Gressl, Lukas; Krisper, Michael; Steger, Christian; Neffe, Ulrich

doi:10.1007/978-3-030-54549-9_25

“…It makes to reduce the feature dimension based on the datasets that are used. Then [11][12][13] propose the hybrid method, which integrates both a Support Vector Machine (SVM) and a Genetic Algorithm (GA).It analyses and extracts the datasets through a learning algorithm, making the genetic algorithm more efficient than the support vector machine. Then the learning algorithm will make associated with feature subset selection.…”

Section: Literature Surveymentioning

confidence: 99%

Untitled

2022

IJCSIT

0

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Cyber-physical Systems based on advanced networks interact with other networks through wireless communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of privacy and security as third-party users should authenticate the network. If it fails, it will create extensive damage and threat to the established network and makes the hacker malfunction the network services efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyberphysical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps to improve the privacy and authentication of the network by improving the data accuracy and Quality of Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the network reliability and security compared to some of the existing SVM-based and Apriori-based detection approaches.

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Effective cyber security system to secure optical data based on deep learning approach for healthcare application

Kanagala

¹

2023

Optik

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Effective Malware Detection Approach based on Deep Learning in Cyber-Physical Systems

Vaddadi¹,

Arnepalli²,

Thatikonda³

et al. 2022

IJCSIT

2

0

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Cyber-physical Systems based on advanced networks interact with other networks through wireless communication to enhance interoperability, dynamic mobility, and data supportability. The vast data is managed through a cloud platform, vulnerable to cyber-attacks. It will threaten the customers in terms of privacy and security as third-party users should authenticate the network. If it fails, it will create extensive damage and threat to the established network and makes the hacker malfunction the network services efficiently. This paper proposes a DL-based CPS approach to identify and mitigate the malware cyberphysical system attack of Denial of Service (DoS) and Distributed Denial of Service (DDoS) as it ensures adequate decision support. At the same time, the trusted user nodes are connected to the network. It helps to improve the privacy and authentication of the network by improving the data accuracy and Quality of Service (QoS) in the network. Here the analysis is determined on the proposed system to improve the network reliability and security compared to some of the existing SVM-based and Apriori-based detection approaches.

show abstract

Towards an Automated Exploration of Secure IoT/CPS Design-Variants

Cited by 3 publications

References 15 publications

Untitled

Untitled

Effective cyber security system to secure optical data based on deep learning approach for healthcare application

Effective Malware Detection Approach based on Deep Learning in Cyber-Physical Systems

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