@inproceedings { , title = {Botnet detection in the Internet of Things using deep learning approaches.}, abstract = {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 used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request.}, conference = {2018 International joint conference on neural networks (IJCNN 2018)}, doi = {10.1109/IJCNN.2018.8489489}, eissn = {2161-4407}, issn = {2161-4393}, note = {COMPLETED -- Now published on IEEE 17/12/2018 LM -- Still not on IEEE 8/10/2018, 21/8/2018, 30/7/2018 LM -- Info via contact 2/5/2018 LM ADDITIONAL INFORMATION: McDermott, Christopher ; Majdani, Farzan ; Petrovski, Andrei -- Panel B}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {http://hdl.handle.net/10059/2894}, keyword = {Deep learning, LSTM, Word embedding, IoT, Botnet, Mirai, DDoS}, year = {2018}, author = {McDermott, Christopher D. and Majdani, Farzan and Petrovski, Andrei V.} }