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.

Approach to Anomaly Traffic Detection in a Local Network

The research intends to solve the problem of the occupation of bandwidth of local network by abnormal traffic which affects normal user's network behaviors.Firstly,a new algorithm in this paper named danger-theory-based abnormal traffic detection was presented.Then an advanced ID3 algorithm was presented to classify the abnormal traffic.Finally a new model of anomaly traffic detection was built upon the two algorithms above and the detection results were integrated with firewall.The firewall limits the bandwidth based on different types of abnormal traffic.Experiments show the outstanding performance of the proposed approach in real-time property,high detection rate,and unsupervised learning.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

Road traffic anomaly monitoring and warning based on DeepWalk algorithm

In the complex urban road traffic network,a sudden accident leads to rapid congestion in the nearby traffic region,which even makes the local traffic network capacity quickly reduce.Therefore,an efficient monitoring system for abnormal conditions of the urban road network plays a crucial role in the tolerance of the urban road network.The traditional traffic monitoring system not only costs a lot in construction and maintenance,but also may not cover the road network comprehensively,which could not meet the basic needs of traffic management.Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly,so that it can provide more effective support for traffic management decisions.The extensive use of positioning equipment made us able to obtain accurate trajectory data.This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data.This model uses deep learning to detect abnormal trajectory on the traffic road network.The method effectively analyses the abnormal source and potential anomaly to judge the abnormal region,which provides an important reference for the traffic department to take effective traffic control measures.Finally,the paper uses Internet vehicle trajectory data from Chengdu(China)to test and obtains an accurate result.