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All Outputs (7)

On the class overlap problem in imbalanced data classification. (2020)
Journal Article
VUTTIPITTAYAMONGKOL, P., ELYAN, E. and PETROVSKI, A. 2021. On the class overlap problem in imbalanced data classification. Knowledge-based systems [online], 212, article number 106631. Available from: https://doi.org/10.1016/j.knosys.2020.106631

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed criti... Read More about On the class overlap problem in imbalanced data classification..

Detection of false command and response injection attacks for cyber physical systems security and resilience. (2020)
Conference Proceeding
EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Detection of false command and response injection attacks for cyber physical systems security and resilience. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 10, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433615

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of... Read More about Detection of false command and response injection attacks for cyber physical systems security and resilience..

Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. (2020)
Conference Proceeding
MAJDANI, F., BATIK, L., PETROVSKI, A. and PETROVSKI, S. 2020. Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 4, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433613

This paper looks at potential vulnerabilities of the Smart Grid energy infrastructure to data injection cyber-attacks and the means of addressing these vulnerabilities through intelligent data analysis. Efforts are being made by multiple groups to pr... Read More about Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data..

Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT). (2020)
Thesis
MCDERMOTT, C.D. 2020. Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT). Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://openair.rgu.ac.uk

The Internet of Things (IoT) is an emerging paradigm, which aims to extend the power of the Internet beyond computers and smartphones to a vast and growing range of "things" - devices, processes and environments. The result is an interconnected world... Read More about Exploring the use of conversational agents to improve cyber situational awareness in the Internet of Things (IoT)..

Handling minority class problem in threats detection based on heterogeneous ensemble learning approach. (2020)
Journal Article
EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Handling minority class problem in threats detection based on heterogeneous ensemble learning approach. International journal of systems and software security and protection [online], 13(3), pages 13-37. Available from: https://doi.org/10.4018/IJSSSP.2020070102

Multiclass problem, such as detecting multi-steps behaviour of Advanced Persistent Threats (APTs) have been a major global challenge, due to their capability to navigates around defenses and to evade detection for a prolonged period of time. Targeted... Read More about Handling minority class problem in threats detection based on heterogeneous ensemble learning approach..

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. (2020)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2020. Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. International journal of applied decision sciences [online], 13(3), pages 344-362. Available from: https://doi.org/10.1504/IJADS.2020.10026404

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the dec... Read More about Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry..

Automated anomaly recognition in real time data streams for oil and gas industry. (2020)
Thesis
MAJDANI SHABESTARI, F. 2020. Automated anomaly recognition in real time data streams for oil and gas industry. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

There is a growing demand for computer-assisted real-time anomaly detection - from the identification of suspicious activities in cyber security, to the monitoring of engineering data for various applications across the oil and gas, automotive and ot... Read More about Automated anomaly recognition in real time data streams for oil and gas industry..