Dr Christopher McDermott's Outputs (2)

Botnet detection in the Internet of Things using deep learning approaches. (2018)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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.