All Outputs (82)

Applications of artificial intelligence in geothermal resource exploration: a review. (2024)
Journal Article
ALGAIAR, M., HOSSAIN, M., PETROVSKI, A., LASHIN, A. and FAISAL, N. 2024. Applications of artificial intelligence in geothermal resource exploration: a review. Deep underground science and engineering [online], 3(3): geothermal energy, pages 269-285. Available from: https://doi.org/10.1002/dug2.12122

Artificial intelligence (AI) has become increasingly important in geothermal exploration, significantly improving the efficiency of resource identification. This review examines current AI applications, focusing on the algorithms used, the challenges... Read More about Applications of artificial intelligence in geothermal resource exploration: a review..

Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. (2024)
Thesis
OTOKWALA, U.J. 2024. Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571244

Critical Infrastructures (CI) are essential for various aspects of human activities, spanning across different sectors. However, the integration of Internet of Things (IoT) devices into CI has introduced a new dimension to security challenges due to... Read More about Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures..

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. (2024)
Presentation / Conference Contribution
OTOKWALA, U., ARIFEEN, M. and PETROVSKI, A. 2024. A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. In Panoutsos, G., Mihaylova, L.S. and Mahfouf, M. (eds.) Advances in computational intelligence systems: contributions presented at the 21st UK workshop on computational intelligence (UKCCI 2022), 7-9 September 2022, Sheffield, UK. Advances in intelligent systems and computing, 1454. Cham: Springer [online], pages 238-249. Available from: https://doi.org/10.1007/978-3-031-55568-8_20

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing... Read More about A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things..

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. (2024)
Journal Article
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2024 Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. International journal of information security [online], 23(4), pages 2559-2581. Available from: https://doi.org/10.1007/s10207-024-00855-7

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performa... Read More about Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things..

FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. (2024)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., AL-KADRI, M.O. and PIRAS, L. 2024. FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 426-441. Available from: https://doi.org/10.1007/978-3-031-54129-2_25

Adhering to security best practices during the development of Android applications is of paramount importance due to the high prevalence of apps released without proper security measures. While automated tools can be employed to address vulnerabiliti... Read More about FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI..

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. (2024)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., PIRAS, L. and AL-KADRI, M.O. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of information security and applications [online], 82, article number 103741. Available from: https://doi.org/10.1016/j.jisa.2024.103741

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to redu... Read More about Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI..

Securing information systems against advanced persistent threats (APTs). (2024)
Thesis
EKE, E.N. 2024. Securing information systems against advanced persistent threats (APTs). Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2445760

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APTs are sophisticated attacks that masquerade their actions to navigate around defenses, breach netw... Read More about Securing information systems against advanced persistent threats (APTs)..

Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and MADZUDZO, G. 2023. Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system. Journal of information security and applications [online], 77, article number 103570. Available from: https://doi.org/10.1016/j.jisa.2023.103570

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate... Read More about Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system..

A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines. (2023)
Journal Article
BANDA, T.M., ZĂVOIANU, A.-C., PETROVSKI, A., WÖCKINGER, D. and BRAMERDORFER, G. 2024. A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines. ACM transactions on evolutionary learning and optimization [online], 4(1), article number 3. Available from: https://doi.org/10.1145/3597618

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving... Read More about A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines..

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. (2023)
Journal Article
LI, Y., REN, J., YAN, Y., LIU, Q., MA, P., PETROVSKI, A. and SUN, H. 2023. CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. IEEE transactions on geoscience and remote sensing [online], 61, 5513011. Available from: https://doi.org/10.1109/TGRS.2023.3276589

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover vari... Read More about CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing..

AI-based intrusion detection systems for in-vehicle networks: a survey. (2023)
Journal Article
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A., MADZUDZO, G. and CHEAH, M. 2023. Al-based intrusion detection systems for in-vehicle networks: a survey. ACM computing survey [online], 55(11), article no. 237, pages 1-40. Available from: https://doi.org/10.1145/3570954

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to nume... Read More about AI-based intrusion detection systems for in-vehicle networks: a survey..

Android source code vulnerability detection: a systematic literature review. (2023)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2023. Android source code vulnerability detection: a systematic literature review. ACM computing surveys [online], 55(9), article 187, pages 1-37. Available from: https://doi.org/10.1145/3556974

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not add... Read More about Android source code vulnerability detection: a systematic literature review..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset] (2022)
Data
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models [Dataset]. Hosted on GitHub (online). Available from: https://github.com/softwaresec-labs/LVDAndro

Many of the Android apps get published without appropriate security considerations, possibly due to not verifying code or not identifying vulnerabilities at the early stages of development. This can be overcome by using an AI based model trained on a... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset].

Framework for detecting APTs based on steps analysis and correlation. (2022)
Book Chapter
EKE, H.N., PETROVSKI, A., AHRIZ, H. and AL-KADRI, M.O. 2022. Framework for detecting APTs based on steps analysis and correlation. In Abbaszadeh, M. and Zemouche, A. (eds.) Security and resilience in cyber-physical systems: detection, estimation and control. Cham: Springer [online], chapter 6, pages 119-147. Available from: https://doi.org/10.1007/978-3-030-97166-3_6

An advanced persistent threatAdvanced persistent threat, (APTAPT), is an attack that uses multiple attack behavior to penetrate a system, achieve specifically targeted and highly valuable goals within a system. This type of attack has presented an in... Read More about Framework for detecting APTs based on steps analysis and correlation..

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..

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)..

A holistic approach to assessment of value of information (VOI) with fuzzy data and decision criteria. (2020)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2020. A holistic approach to assessment of value of information (VOI) with fuzzy data and decision criteria. Decision making: applications in management and engineering [online], 3(2), pages 97-118. Available from: https://doi.org/10.31181/dmame2003097v

Classical decision and value of information theories have been applied in the oil and gas industry from the 1960s with partial success. In this research, we identify that the classical theory of value of information has weaknesses related with optima... Read More about A holistic approach to assessment of value of information (VOI) with fuzzy data and decision criteria..

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..