Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

Nowadays,web systems and servers are constantly at great risk from cyberattacks.This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory(LSTM)network in combination with the ensemble learning technique.First,the binary classification module was used to detect the current abnormal flow.Then,the abnormal flows were fed into the multilayer classification module to identify the specific type of flow.In this research,a deep learning bidirectional LSTM model,in combination with the convolutional neural network and attention technique,was deployed to identify a specific attack.To solve the real-time intrusion-detecting problem,a stacking ensemble-learning model was deployed to detect abnormal intrusion before being transferred to the attack classification module.The class-weight technique was applied to overcome the data imbalance between the attack layers.The results showed that our approach gained good performance and the F1 accuracy on the CICIDS2017 data set reached 99.97%,which is higher than the results obtained in other research.