Skip to main content

Research Repository

Advanced Search

All Outputs (5)

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..

Towards a conversational agent for threat detection in the internet of things. (2019)
Conference Proceeding
MCDERMOTT, C.D., JEANNELLE, B. and ISAACS, J.P. 2019. Towards a conversational agent for threat detection in the internet of things. In Proceedings of the 2019 International Cyber science on cyber situational awareness, data analytics and assessment (Cyber SA): pioneering research and innovation in cyber situational awareness, 3-4 June 2019, Oxford, UK. Piscataway: IEEE [online], chapter 6. Available from: https://doi.org/10.1109/CyberSA.2019.8899580

A conversational agent to detect anomalous traffic in consumer IoT networks is presented. The agent accepts two inputs in the form of user speech received by Amazon Alexa enabled devices, and classified IDS logs stored in a DynamoDB Table. Aural anal... Read More about Towards a conversational agent for threat detection in the internet of things..

Dimensions of ‘socio’ vulnerabilities of advanced persistent threats. (2019)
Conference Proceeding
NICHO, M. and MCDERMOTT, C.D. 2019. Dimensions of ‘socio’ vulnerabilities of advanced persistent threats. In Begušić, D., Rožić, N., Radić, J. and Šarić, M. (eds.) Proceedings of the 27th International software, telecommunications and computer networks conference 2019 (SoftCOM 2019), 19-21 September 2019, Split, Croatia. Piscataway: IEEE [online], article ID 8903788. Available from: https://doi.org/10.23919/SOFTCOM.2019.8903788

Advanced Persistent Threats (APT) are highly targeted and sophisticated multi-stage attacks, utilizing zero day or near zero-day malware. Directed at internetworked computer users in the workplace, their growth and prevalence can be attributed to bot... Read More about Dimensions of ‘socio’ vulnerabilities of advanced persistent threats..

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..

Towards situational awareness of botnet activity in the Internet of Things (2018)
Conference Proceeding
MCDERMOTT, C.D., PETROVSKI, A.V. and MAJDANI, F. 2018. Towards situational awareness of botnet activity in the Internet of Things. In Proceedings of the 2018 International conference on cyber situational awareness, data analytics and assessment (Cyber SA 2018): cyber situation awareness as a tool for analysis and insight, 11-12 June 2018, Glasgow, UK. Piscataway: IEEE [online], article number 8551408. Available from: https://doi.org/10.1109/CyberSA.2018.8551408

An IoT botnet detection model is designed to detect anomalous attack traffic utilised by the mirai botnet malware. The model uses a novel application of Deep Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTMRNN), in conjuncti... Read More about Towards situational awareness of botnet activity in the Internet of Things.