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.
The authors of REF proposed using deep learning in developing a botnet detection model according to which the packets are converted into tokenized integer format by word embedding and inputted to a bidirectional long short term memory-based recurrent neural network (BLSTM-RNN) for learning from and detecting of botnets.
Botnet Detection in the Internet of Things using Deep Learning Approaches
2018 International Joint Conference on Neural Networks (IJCNN)
2018 International Joint Conference on Neural Networks (IJCNN)
Computer Science