All Outputs (67)

Effective detection of cyber attack in a cyber-physical power grid system. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Effective detection of cyber attack in a cyber-physical power grid system. In Arai, K. (ed) Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021), 29-30 April 2021, Vancouver, Canada. Advances in intelligent systems and computing, 1363. Cham: Springer [online], 1, pages 812-829. Available from: https://doi.org/10.1007/978-3-030-73100-7_57

Advancement in technology and the adoption of smart devices in the operation of power grid systems have made it imperative to ensure adequate protection for the cyber-physical power grid system against cyber-attacks. This is because, contemporary cyb... Read More about Effective detection of cyber attack in a cyber-physical power grid system..

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..

Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. (2019)
Conference Proceeding
ZAKARIYYA, I., AL-KADRI, M.O., KALUTARGE, H. and PETROVSKI, A. 2019. Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. In Obaidat, M. and Samarati, P. (eds.) Proceedings of the 16th International security and cryptography conference (SECRYPT 2019), co-located with the 16th International joint conference on e-business and telecommunications (ICETE 2019), 26-28 July 2019, Prague, Czech Republic. Setúbal, Portugal: SciTePress [online], 2, pages 523-528. Available from: https://doi.org/10.5220/0008119205230528.

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to i... Read More about Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm..

The use of machine learning algorithms for detecting advanced persistent threats. (2019)
Conference Proceeding
EKE, H.N., PETROVSKI, A. and AHRIZ, H. 2019. The use of machine learning algorithms for detecting advanced persistent threats. In Makarevich, O., Babenko, L., Anikeev, M., Elci, A. and Shahriar, H. (eds.). Proceedings of the 12th Security of information and networks international conference 2019 (SIN 2019), 12-15 September 2019, Sochi, Russia. New York: ACM [online], article No. 5. Available from: https://doi.org/10.1145/3357613.3357618

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. Due to their capability to navigates around defenses and to evade detection for a prolonged period of... Read More about The use of machine learning algorithms for detecting advanced persistent threats..

Fuzzy logic applied to value of information assessment in oil and gas projects. (2019)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2019. Fuzzy logic applied to value of information assessment in oil and gas projects. Petroleum science [online], 16(5), pages 1208-1220. Available from: https://doi.org/10.1007/s12182-019-0348-0

The concept of value of information (VOI) has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields. The classical approach to VOI assumes that the outcome of th... Read More about Fuzzy logic applied to value of information assessment in oil and gas projects..

A fuzzy inference system applied to value of information assessment for oil and gas industry. (2019)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2019. A fuzzy inference system applied to value of information assessment for oil and gas industry. Decision making: applications in management and engineering [online], 2(2), pages 1-18. Available from: https:// doi.org/10.31181/dmame1902001v

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 A fuzzy inference system applied to value of information assessment for oil and gas industry..

Evaluating awareness and perception of botnet activity within consumer Internet-of-Things (IoT) networks. (2019)
Journal Article
MCDERMOTT, C.D., ISAACS, J.P. and PETROVSKI, A.V. 2019. Evaluating awareness and perception of botnet activity within consumer Internet-of-Things (IoT) networks. Informatics [online], 6(1), article 8. Available from: https://doi.org/10.3390/informatics6010008

The growth of the Internet of Things (IoT), and demand for low-cost, easy-to-deploy devices, has led to the production of swathes of insecure Internet-connected devices. Many can be exploited and leveraged to perform large-scale attacks on the Intern... Read More about Evaluating awareness and perception of botnet activity within consumer Internet-of-Things (IoT) networks..

Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation. (2019)
Journal Article
OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2019. Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation. Journal of cloud computing [online], 8, article 1. Available from: https://doi.org/10.1186/s13677-018-0124-5

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workloa... Read More about Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation..

Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis. (2018)
Journal Article
OCHEI, L.C., BASS, J.M. and PETROVSKI, A. 2018. Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis. Journal of cloud computing [online], 7, article ID 22. Available from: https://doi.org/10.1186/s13677-018-0121-8

A challenge, when implementing multi-tenancy in a cloud-hosted software service, is how to ensure that the performance and resource consumption of one tenant does not adversely affect other tenants. Software designers and architects must achieve an o... Read More about Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis..

Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP). (2018)
Journal Article
ANI, M., OLUYEMI, G., PETROVSKI, A. and REZAEI-GOMARI, S. 2019. Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP). Journal of petroleum science and engineering [online], 174, pages 833-843. Available from: https://doi.org/10.1016/j.petrol.2018.11.024

The selection of an optimal model from a set of multiple realizations for dynamic reservoir modelling and production forecasts has been a persistent issue for reservoir modelers and decision makers. Current evidence has shown that many presumably goo... Read More about Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP)..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service. (2018)
Journal Article
OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2018. A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service. International journal of cloud computing [online], 7(3-4), pages 248-281. Available from: https://doi.org/10.1504/IJCC.2018.095396

Multitenancy allows multiple tenants to access a single instance of a cloud offering. While several approaches exist for implementing multitenancy, little attention has been paid to implementing the required degree of isolation since there are varyin... Read More about A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service..

Generic application of deep learning framework for real-time engineering data analysis. (2018)
Conference Proceeding
MAJDANI, F., PETROVSKI, A. and PETROVSKI, S. 2018. Generic application of deep learning framework for real-time engineering data analysis. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489356. Available from: https://doi.org/10.1109/IJCNN.2018.8489356

The need for computer-assisted real-time anomaly detection in engineering data used for condition monitoring is apparent in various applications, including the oil and gas, automotive industries and many other engineering domains. To reduce the relia... Read More about Generic application of deep learning framework for real-time engineering data analysis..

Botnet detection in the Internet of Things using deep learning approaches. (2018)
Conference Proceeding
MCDERMOTT, C.D., MAJDANI, F. and PETROVSKI, A.V. 2018. Botnet detection in the Internet of Things using deep learning approaches. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489489. Available from: https://doi.org/10.1109/IJCNN.2018.8489489

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is u... Read More about Botnet detection in the Internet of Things using deep learning approaches..