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Replacing human input in spam email detection using deep learning. (2022)
Conference Proceeding
NICHO, M., MAJDANI, F. and MCDERMOTT, C.D. 2022. Replacing human input in spam email detection using deep learning. In Degen, H. and NTOA, S. (eds.) Artificial intelligence in HCI: proceedings of 3rd International conference on artificial intelligence in HCI (human-computer interaction) 2022 (AI-HCI 2022), co-located with the 24th International conference on human-computer interaction 2022 (HCI International 2022), 26 June - 1 July 2022, [virtual conference]. Lecture notes in artificial intelligence (LNAI), 13336. Cham: Springer [online], pages 387-404. Available from: https://doi.org/10.1007/978-3-031-05643-7_25

The Covid-19 pandemic has been a driving force for a substantial increase in online activity and transactions across the globe. As a consequence, cyber-attacks, particularly those leveraging email as the preferred attack vector, have also increased e... Read More about Replacing human input in spam email detection using deep learning..

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..

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..