Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

Detecting While Accessing:A Semi-Supervised Learning-Based Approach for Malicious Traffic Detection in Internet of Things

In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively.

C2Net-YOLOv5: A Bidirectional Res2Net-Based Traffic Sign Detection Algorithm

Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD domains.In this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed.An enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information complementarity.Furthermore,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed methodology.The performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark 2021.On the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,respectively.Similarly,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,respectively.Experimental results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.

Research on Traffic Sign Detection Based on Improved YOLOv8

Aiming at solving the problem of missed detection and low accuracy in detecting traffic signs in the wild, an improved method of YOLOv8 is proposed. Firstly, combined with the characteristics of small target objects in the actual scene, this paper further adds blur and noise operation. Then, the asymptotic feature pyramid network (AFPN) is introduced to highlight the influence of key layer features after feature fusion, and simultaneously solve the direct interaction of non-adjacent layers. Experimental results on the TT100K dataset show that compared with the YOLOv8, the detection accuracy and recall are higher. .

Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.

A Deep Learning Model of Traffic Signs in Panoramic Images Detection

To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.

Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation

Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving.This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively.Using 2D region proposals in an RGB image,this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network(FPS-Net)and feature extraction network(FE-Net).Subsequently,the encoder-decoder network(ED-Net)implements 3D-oriented bounding box(OBB)regression.Meanwhile,the adaptive least square regression(ALSR)method is proposed to split 3D OBB.Finally,the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem(SST).In the experiments of KITTI benchmark,our proposed 3D object detector outperforms other state-of-theartmethods.Meanwhile,collision detection algorithm achieves the satisfactory performance of 91.8%accuracy on our SHTA dataset.

LLTH‑YOLOv5:A Real‑Time Traffic Sign Detection Algorithm for Low‑Light Scenes

Traffic sign detection is a crucial task for autonomous driving systems.However,the performance of deep learning-based algorithms for traffic sign detection is highly affected by the illumination conditions of scenarios.While existing algo-rithms demonstrate high accuracy in well-lit environments,they suffer from low accuracy in low-light scenarios.This paper proposes an end-to-end framework,LLTH-YOLOv5,specifically tailored for traffic sign detection in low-light scenarios,which enhances the input images to improve the detection performance.The proposed framework comproses two stages:the low-light enhancement stage and the object detection stage.In the low-light enhancement stage,a lightweight low-light enhancement network is designed,which uses multiple non-reference loss functions for parameter learning,and enhances the image by pixel-level adjustment of the input image with high-order curves.In the object detection stage,BIFPN is introduced to replace the PANet of YOLOv5,while designing a transformer-based detection head to improve the accuracy of small target detection.Moreover,GhostDarkNet53 is utilized based on Ghost module to replace the backbone network of YOLOv5,thereby improving the real-time performance of the model.The experimental results show that the proposed method significantly improves the accuracy of traffic sign detection in low-light scenarios,while satisfying the real-time requirements of autonomous driving.

A Hybrid Feature Fusion Traffic Sign Detection Algorithm Based on YOLOv7

Autonomous driving technology has entered a period of rapid development,and traffic sign detection is one of the important tasks.Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced,and traffic sign targets are small and have unclear features,which makes detection more difficult.Therefore,we propose aHybrid Feature Fusion Traffic Sign detection algorithmbased onYOLOv7(HFFTYOLO).First,a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales;Secondly,the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method.Design reuse paths at the end of the neck,paying particular attention to cross-scale fusion of highlevel features.In addition,we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters.In terms of training,a newregression lossCMPDIoUis proposed,which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different,but also enables the penalty term to dynamically change at different scales.Finally,our proposed improved method shows excellent results on the TT100K dataset.Compared with the baseline model,without increasing the number of parameters and computational complexity,AP0.5 and AP increased by 2.2%and 2.7%,respectively,reaching 92.9%and 58.1%.

A Method for Detecting Wide-scale Network Traffic Anomalies

Network traffic anomalies refer to the traffic changed abnormally and obviously.Local events such as temporary network congestion,Distributed Denial of Service(DDoS)attack and large-scale scan,or global events such as abnormal network routing,can cause network anomalies.Network anomaly detection and analysis are very important to Computer Security Incident Response Teams(CSIRT).But wide-scale traffic anomaly detection requires extracting anomalous modes from large amounts of high-dimensional noise-rich data,and interpreting the modes;so,it is very difficult.This paper proposes a general method based on Principle Component Analysis(PCA)to analyze network anomalies.This method divides the traffic matrix into normal and anomalous subspaces,maps traffic vectors into the normal subspace,gets the distance from detected vector to average normal vector,and detects anomalies based on that distance.

A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection attacks.Most DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing resources.This paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to victims.It defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection attacks.BLCD also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS packets.For evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence locations.Experiments show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

Social Media Based Transportation Research: the State of the Work and the Networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources.In this paper, we review social media based transportation research with social network analysis methods. We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively.Finally, some future research directions are identified.

Real-time detection network for tiny traffic sign using multi-scale attention module

As one of the key technologies of intelligent vehicles, traffic sign detection is still a challenging task because of the tiny size of its target object. To address the challenge, we present a novel detection network improved from yolo-v3 for the tiny traffic sign with high precision in real-time. First, a visual multi-scale attention module(MSAM), a light-weight yet effective module, is devised to fuse the multi-scale feature maps with channel weights and spatial masks. It increases the representation power of the network by emphasizing useful features and suppressing unnecessary ones. Second, we exploit effectively fine-grained features about tiny objects from the shallower layers through modifying backbone Darknet-53 and adding one prediction head to yolo-v3. Finally, a receptive field block is added into the neck of the network to broaden the receptive field. Experiments prove the effectiveness of our network in both quantitative and qualitative aspects. The m AP@0.5 of our network reaches 0.965 and its detection speed is55.56 FPS for 512 × 512 images on the challenging Tsinghua-Tencent 100 k(TT100 k) dataset.

Traffic light detection and recognition for autonomous vehicles

Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.

Effects of odd-even traffic restriction on travel speed and traffic volume:Evidence from Beijing Olympic Games

This paper reports the effects of using an ＂odd and even＂ traffic restriction policy in Beijing during the 2008 Olympic Games. Based on data from 529 traffic detectors on the expressway network and some main arterials in Beijing, China, a comparative analysis has been carried out on the following parameters： the total traffic volume within the expressway network, the total traffic volume on different ring expressways, the traffic volume and speed of a freeway segment, and an arterial street before and after the implementation of the traffic restriction policy. The results show that during the traffic restriction period, although more than 50% of vehicles were forbidden to travel in Beijing, the traffic volume was only reduced by 20%-40% while the travel speed had been increased by 10%-20%. This suggests that such traffic restriction policy may be an effective shortterm management measure in dealing with increased transportation demand and congestion during major events, such as the Olympic Games. Results also indicate that vehicle travel demand does not decrease with the same proportion as the total vehicles forbidden, at least for the expressway and main arterials in a city.

Intelligent Video Surveillance for Checking Attendance of Traffic Controllers in Level Crossing

This paper proposes a detecting and tracking scheme for automatic checking attendance of traffic controllers in level crossing by recognizing their warning waistcoats. Considering of the actual requirement of rapidity and validity, this paper employs techniques of motion detection, color segmentation and feature matching to deal with the challenging problems of illumination varying, light reflection and disturbance. Therefore, the task of distinguishing the target from candidates can be fulfilled accurately. Once a target being detected, the established color models are modified through learning color of the detected target, and then Cam-shift algorithm is employed to track this target smoothly. The experiments in real scenes demonstrate that this method has a great capability to detect and track traffic controllers in complex level crossing environment accurately, and the comparisons further demonstrate the validity of the proposed method.

Intelligent Multi-objective Anomaly Detection Method Based on Robust Sparse Flow

To meet the needs of transportation systems for smart scenic security services,real-time detection and identification of traffic anomalies with high accuracy is essential.Based on the multi-objective sparse optical flow estimation method based on KLT algorithm,an improved algorithm for robust sparse optical flow is designed.The Forward-Backward error calculation method was used to eliminate the error optical flow generated by the KLT algorithm and the robustness of optical flow was improved.The proposed algorithm was verified by the actual traffic scene monitoring example,and the anomaly detection accuracy is above 80%.Furthermore,it has good detection effect on the benchmark dataset.

YOLOP:You Only Look Once for Panoptic Driving Perception

A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.