Abstract
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules. However, these solutions can become reliable and effective when integrated with artificial intelligence (AI) based techniques. During the last few years, deep learning models especially convolutional neural networks achieved high significance due to their outstanding performance in the image processing field. The potential of these convolutional neural network (CNN) models can be used to efficiently detect the complex DoS and DDoS by converting the network traffic dataset into images. Therefore, in this work, we proposed a methodology to convert the network traffic data into image form and trained a state-of-the-art CNN model, i.e., ResNet over the converted data. The proposed methodology accomplished 99.99% accuracy for detecting the DoS and DDoS in case of binary classification. Furthermore, the proposed methodology achieved 87% average precision for recognizing eleven types of DoS and DDoS attack patterns which is 9% higher as compared to the state-of-the-art.
Highlights
Internet of Things (IoT) is the wireless interconnection of smart devices or things connected over the internet
In order to handle this issue, we proposed a methodology to convert the network traffic captures, i.e., non-image data into a representable form, i.e., image form and trained a state-of-the-art convolutional neural network (CNN) model over the converted data in order to better detect the Denial of service (DoS) and distributed denial of service (DDoS) attack patterns
For getting the efficient performance of residual network (ResNet) [13], we proposed a methodology to convert the non-image network traffic dataset into three-channel images
Summary
Internet of Things (IoT) is the wireless interconnection of smart devices or things connected over the internet. In order to handle this issue, we proposed a methodology to convert the network traffic captures, i.e., non-image data into a representable form, i.e., image form and trained a state-of-the-art CNN model over the converted data in order to better detect the DoS and DDoS attack patterns. In order to better detect the complex DoS and DDoS attacks, we used a state-of-the-art CNN model, i.e., ResNet [13] which showed efficient performance in detecting the image patterns. The proposed methodology showed better results for detecting the DoS and DDoS attack patterns as compared to the state-of-the-art work.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
