Cybersecurity as a Centralized Directed System of Systems Using SoS Explorer as a Tool

Ashiku, Lirim; Dağli, Ci̇han H.

doi:10.1109/sysose.2019.8753872

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…The PH model is used as a guide for development of the CS-S factor structure. Table 1 shows the definition of the six dimensions adapted from Ashiku and Dagli (2019, p.143).…”

Section: Theoretical Background and Development Of The Scale Itemsmentioning

confidence: 99%

Development of the cybersecurity scale (CS-S): Evidence of validity and reliability

Arpacı

Sevinc

2021

Information Development

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This study aimed to develop a cybersecurity scale to measure individuals’ practices and perceptions regarding cybersecurity. The study tested psychometric properties of the Cybersecurity Scale (CS-S) by employing a multi-stage research design. In the first study, an Exploratory-Factor-Analysis (EFA) was conducted to explore the underlying factor structure and evaluate internal consistency reliability of the CS-S. The EFA results showed good internal consistency reliability (α = .88) and a six-factor structure. In the second study, a Confirmatory-Factor-Analysis (CFA) was conducted to verify the factor structure. The CFA results indicated that the six-factor model (i.e., confidentiality, control/possession, integrity, authenticity, availability, and utility) fits the data well. Significant individual differences were observed in each dimension of the CS-S. Results indicated that the CS-S has evidence of convergent, discriminant, and construct validity along with internal consistency reliability. The results suggested that the CS-S is a valid and reliable instrument to measure individuals’ cybersecurity practices and perceptions.

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“…The PH model is used as a guide for development of the CS-S factor structure. Table 1 shows the definition of the six dimensions adapted from Ashiku and Dagli (2019, p.143).…”

Section: Theoretical Background and Development Of The Scale Itemsmentioning

confidence: 99%

Development of the cybersecurity scale (CS-S): Evidence of validity and reliability

Arpacı

Sevinc

2021

Information Development

View full text Add to dashboard Cite

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“…Cyber security is becoming an issue of worldwide interest and importance, and it entails safeguarding data by detecting, avoiding, and responding to cyber-attacks. Various firms' defensive methods for protecting their cyberspace are insufficient to defend their cyber environments from ever-increasing security risks [26]. As a result, it is one of the significant scientific concerns that has drawn the attention of scholars and practitioners during the previous decade.…”

Section: Introducing the Cparr Architecture In Place For Security Man...mentioning

confidence: 99%

Cyber Security Threats, Vulnerabilities, and Security Solutions Models in Banking

Ghelani

Hua²,

Koduru

2022

Preprint

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“…This system was accurate 96.4 percent of the time. A GA and SVM were suggested by Ashiku et al [40] to be used to find the intrusion packets. Select features were combined using SVM and GA.…”

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confidence: 99%

Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection

2023

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Traditional firewalls and data encryption techniques can no longer match the demands of current IoT network security due to the rising amount and variety of network threats. In order to manage IoT network risks, intrusion detection solutions have been advised. Even though machine learning (ML) helps the widely used intrusion detection techniques currently in use, these algorithms struggle with low detection rates and the requirement for extensive feature engineering. The deep learning model for IoT network intrusion detection is a method for traffic anomaly detection that is suggested by this study. To extract the sequence properties of data flow through a CNN, it combines an attention mechanism with a Long Short Term Memory (LSTM) network. This method uses adaptive synthetic sampling (ADASYN) to increase the size of minority-class samples. The proposed models demonstrated acceptable precision and recall for each class when used for binary-class classification, proving their stability and capacity to identify all classes correctly. The MLP classifier’s accuracy, precision, recall, and F1 value were 87%, 89%, 87%, and 89%, respectively, with an AUC score of 0.88. Overall, the proposed models performed well. The attack and all-class models exhibited good AUCs and macro metrics, the same as the proposed MLP classifier, which had an F1 score of 83% and an AUC score of 0.94. Additionally, it trained the MLP classifier and integrated the ADAM optimizer and category cross-entropy loss function for all-class classification. With an AUC value of 94%, it possessed 84% accuracy, 87% precision, 84% recall, and an 83% F1 score. A further indication of the hybrid model’s ability to combine the benefits of both models to improve overall performance was that it regularly outperformed the MLP model. This model’s accuracy and F1 score are better than those of earlier comparable algorithms, according to experimental results using the publicly accessible benchmark dataset for network intrusion detection (NSL–KDD).

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Cybersecurity as a Centralized Directed System of Systems Using SoS Explorer as a Tool

Cited by 16 publications

References 14 publications

Development of the cybersecurity scale (CS-S): Evidence of validity and reliability

Development of the cybersecurity scale (CS-S): Evidence of validity and reliability

Cyber Security Threats, Vulnerabilities, and Security Solutions Models in Banking

Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection

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