A Deep Learning-Based DDoS Detection Framework for Internet of Things
Li Ma, Ying Chai, Lei Cui, Dongchao Ma, Yingxun Fu, Ailing Xiao
Abstract
Intrusion detection system (IDS) is an active defense mechanism implemented by the Internet of Things (IoT), which can identify the intrusion behavior and initiate alarms. However, there are concerns regarding the sustainability and feasibility to existing schemes when facing the increasing of threats in IoT. In particular, these concerns in terms of the increasing levels of adaptive performance and the insufficient levels of detection accuracy. In this paper, we present a novel deep learning method to address the aforementioned concerns. We detail the proposed convolution neural network model based on the developed feature fusion mechanism. Furthermore, we also propose a Symmetric logarithmic loss function based on categorical cross entropy. In addition, the proposed detection framework has been applied to GPU-enabled TensorFlow, and evaluated using the benchmark of NSL-KDD datasets. Extensive experimental results indicate that the developed model outperforms traditional approaches and has great potential to be applied for attacks detection in IoTs.