Search Results (228)

Air traffic is increasingly complicated, and with the expansion of the aviation industry, a growing emphasis on the safety of flight is being driven. According to flight experience and safety regulation standards, flight abnormal behavior is typically manifested through trajectories as well as other behavioral characteristics. Trajectory anomaly detection is a critical component for ensuring flight safety. This paper presents a comprehensive review that covers flight abnormal behavior analysis and trajectory anomaly detection. The definition of flight abnormal behavior and trajectory is clarified at first. Then, this paper proposes a framework of anomaly detection in flight trajectory. On this basis, the review expounds upon the methodologies that have been employed in three primary types of trajectory anomaly detection: speed anomalies, altitude anomalies, and heading deviations. The main applications in this field consist of anomaly warning, online real-time anomaly detection, and the quantitative evaluation of flight abnormal behavior. Future research should encompass studies on the classification of flight traffic behavior classification, the integration of flight trajectory, and other data sources to identify flight abnormal behaviors. This study contributes to furnish more actionable insights for the advancement of trajectory anomaly detection technologies, offering significant implications for an in-depth comprehension of flight abnormal behavior. Full article

Intrusion detection aims to effectively detect abnormal attacks in Internet of Things (IoT) networks, which is crucial for cybersecurity. However, it is difficult for traditional intrusion detection methods to effectively extract data features from traffic data, and most existing models are too complex to be deployed on edge servers. Addressing this need, this paper proposes a hybrid feature selection method and a lightweight deep learning intrusion detection model. Firstly, the data feature space is reduced using variance filtering, mutual information, and the Pearson Correlation Coefficient, thereby reducing the computational cost of subsequent model training. Then, an intrusion detection model based on a Temporal Convolutional Network (TCN) is constructed. This model utilizes dilated causal convolutions to effectively capture long-term temporal dependencies in network traffic. Simultaneously, the residual connections are used to mitigate the vanishing gradient problem, making the model easier to train and converge. Finally, experiments are conducted on the newly released Edge-IIoTset dataset. The results show that the proposed feature selection algorithm maintains good detection performance despite a significant reduction in feature dimensionality. Furthermore, compared with other models, the proposed TCN-based approach achieves higher classification accuracy with lower computational overhead, demonstrating its suitability for deployment in resource-constrained edge computing environments. Full article

With the rapid growth of large-scale networks, traditional rule-based and supervised anomaly detection methods struggle with heavy reliance on labeled data, slow response to rapidly changing patterns, and difficulty in capturing complex temporal anomalies. At the same time, real-world traffic exhibits strong class imbalance, where normal samples overwhelmingly dominate, causing many existing models to miss subtle but critical abnormal behaviors. To address these challenges, this paper proposes an unsupervised temporal anomaly detection framework for network traffic based on a Transformer-autoencoder bidirectional prediction and reconstruction model. The framework combines the advantages of autoencoders and regression models, using multi-head self-attention and positional encoding to capture long-range temporal dependencies in traffic sequences. A masked decoding mechanism is further employed to prevent information leakage from future time steps. The model jointly generates forward and backward predictions as well as reconstructed sequences, and designs multiple anomaly scoring strategies that integrate prediction and reconstruction errors to enhance the sensitivity to point, contextual, and collective anomalies under highly imbalanced data. Experiments on three public benchmark datasets demonstrate that the proposed method significantly improves detection performance, achieving up to an F1 score of 0.960 and a precision of 0.949, with recall approaching 1.0, while reducing false alarms, thereby showing strong applicability to practical network security scenarios. Full article

The spatial and temporal conflicts within terminal maneuvering areas, particularly in multi-airport systems, are growing increasingly complex. Traditional independent processing methods face inherent limitations when dealing with multi-source uncertainties, dynamic weather conditions, and high-density operations. This paper proposes MSTAGNN-MARL that systematically integrates the resolution of spatial conflicts and temporal scheduling issues. This framework is based on four crucial innovations: First, a strategic-tactical-execution hierarchical architecture is constructed that integrates multi-criteria decision optimization with graph neural network-based multi-agent reinforcement learning. Second, an uncertainty perception mechanism is designed that explicitly encodes conflict features as dynamic edge attributes in social graphs, incorporating a real-time dynamic weather model and a Gaussian noise-based perception uncertainty model. Third, develop a compliance automated system for behavior cloning that learns the decision preferences of controllers to achieve human–machine collaboration and provide transparent visualization. Fourth, a robustness assurance mechanism for abnormal scenarios is constructed, employing behavior tree-driven emergency strategies to handle unexpected situations. Experiments demonstrate that the proposed method achieves an 89.3% conflict resolution rate, reduces average delays by 6 min compared to existing methods, and exhibits robust performance under varying traffic densities and dynamic weather conditions. Ablation experiments validate the effectiveness of the four innovations. This framework provides a new research paradigm for scheduling and decision-making in Intelligent Transportation Systems (ITS). Full article

With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed, coordinated, and intertwined across heterogeneous data modalities, posing substantial challenges to data-driven platform governance and early risk identification. Existing approaches often fail to jointly model cross-modal temporal semantics, the gradual evolution of weak abnormal signals, and organized group-level manipulation. To address these challenges, a data-driven multimodal abnormal behavior detection framework, termed MM-FGDNet, is proposed for large-scale live-streaming environments. The framework models abnormal behaviors from two complementary perspectives, namely temporal evolution and cooperative group structure. A cross-modal temporal alignment module first maps video, text, audio, and user behavioral signals into a unified temporal semantic space, alleviating temporal misalignment and semantic inconsistency across modalities. Building upon this representation, a temporal fraud pattern modeling module captures the progressive transition of abnormal behaviors from early incipient stages to abrupt outbreaks, while a cooperative manipulation detection module explicitly identifies coordinated interactions formed by organized user groups and automated accounts. Extensive experiments on real-world multi-platform live-streaming e-commerce datasets demonstrate that MM-FGDNet consistently outperforms representative baseline methods, achieving an AUC of 0.927 and an F1 score of 0.847, with precision and recall reaching 0.861 and 0.834, respectively, while substantially reducing false alarm rates. Moreover, the proposed framework attains an Early Detection Score of 0.689. This metric serves as a critical benchmark for operational viability, quantifying the system’s capacity to shift platform governance from passive remediation to proactive prevention. It confirms the reliable identification of the “weak-signal” stage—rigorously defined as the incipient phase where subtle, synchronized deviations in interaction rhythms manifest prior to traffic inflation outbreaks—thereby providing the necessary time window for preemptive intervention against coordinated manipulation. Ablation studies further validate the independent contributions of each core module, and cross-domain generalization experiments confirm stable performance across new streamers, new product categories, and new platforms. Overall, MM-FGDNet provides an effective and scalable data-driven artificial intelligence solution for early detection of coordinated abnormal behaviors in live-streaming systems. Full article

As cyberattacks become more intelligent and diverse, enterprises’ digital assets face greater challenges. We developed a learning-based endpoint attack detection system (DLEADS) that continuously monitors CPU usage, memory load, disk I/O, network traffic, and other system metrics. By feeding data into a convolutional neural network, the system presents high accuracy in detecting abnormal behavior and classifying various attack types, enabling early warning and rapid incident response. DLEADS demonstrates high performance on real-world enterprise datasets, offering a practical solution for automated cybersecurity management. Full article

With the widespread adoption of the Internet of Things (IoT) and smart devices, the volume of data generated in sensor networks has increased dramatically, with diverse and structurally complex types that pose growing security risks. Anomaly detection in sensor networks has become a key technology for ensuring system stability and secure operation. This paper proposes a sensor anomaly detection model, termed RESTADM, which integrates a Transformer and a Radial Basis Function (RBF) neural network. The model first employs the Transformer to effectively capture the temporal dependencies in sensor data and then uses the RBF neural network to accurately identify anomalies. Experimental results on two public benchmark datasets, SMD and PSM, demonstrate the state-of-the-art performance of RESTADM. Our model achieves impressive F1-scores of 98.56% on SMD and 97.70% on PSM. This represents a statistically significant improvement compared to a range of baseline algorithms, including traditional models like CNN and LSTM, as well as the standard Transformer model. This validates the effectiveness of our proposed Transformer-RBF fusion, confirming the model’s high accuracy and robustness and offering an efficient security solution for intelligent sensing systems. Full article

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms. Full article

Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these challenges, this paper proposes an improved attention-mechanism spatio-temporal graph convolutional network, termed AMSGCN, for highway traffic flow prediction. AMSGCN introduces an adaptive adjacency matrix learning mechanism to overcome the limitations of static graphs and capture time-varying spatial correlations and congestion propagation paths. A hierarchical multi-scale spatial attention mechanism is further designed to jointly model local congestion diffusion and long-range bottleneck effects, enabling an adaptive spatial receptive field under congested conditions. To enhance temporal modeling, a gating-based fusion strategy dynamically balances periodic patterns and recent observations, allowing effective prediction under both regular and abnormal traffic scenarios. In addition, direction-aware encoding is incorporated to suppress interference from opposite-direction lanes, which is essential for directional highway traffic systems. Extensive experiments on multiple benchmark datasets, including PeMS and PEMSF, demonstrate the effectiveness and robustness of AMSGCN. In particular, on the I-24 MOTION dataset, AMSGCN achieves an RMSE reduction of 11.0% compared to ASTGCN and 17.4% relative to the strongest STGCN baseline. Ablation studies further confirm that dynamic and multi-scale spatial attention provides the primary performance gains, while temporal gating and direction-aware modeling offer complementary improvements. These results indicate that AMSGCN is a robust and effective solution for highway traffic flow prediction. Full article

Road traffic significantly contributes to fuel consumption and emissions. Fortunately, the advent of cooperative adaptive cruise control (CACC), facilitated by vehicle-to-vehicle (V2V) communication, reduces energy consumption and improves efficiency in transportation systems. Nevertheless, V2V communication performance (V2VCP) is highly vulnerable to degradation due to various factors. Limited comprehension exists regarding the generalized modeling of subpar V2V communication performance (SV2VCP), coupled with limited exploration of its resulting impacts on environmental sustainability. To bridge these gaps, this study presents the first attempt to assess the impact of SV2VCP on fuel consumption and exhaust emissions within the CACC framework. More specifically, we adopt the multi-predecessor following (MPF) topology and model SV2VCP scenarios, along with proposing five vehicle state update methods (VSUMs). Subsequently, by simulating various SV2VCP and driving scenarios, we comprehensively understand the effects of different VSUMs, SV2VCP, and abnormal vehicle positions on the safety, emissions, and energy consumption of the platoon. The results reveal that SV2VCP substantially impacts the fuel efficiency and emission performance of the CACC platoon, with fuel consumption during deceleration exceeding that of acceleration by approximately 14% when all vehicles are subject to SV2VCP. Furthermore, our study provides critical recommendations for optimal strategy selection, aiming to foster energy conservation and emission reductions, thereby promoting sustainable transport systems. Full article

Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a ship behavior recognition method that integrates trajectory-to-image conversion with a convolutional neural network (CNN) for classifying three typical motion states: mooring, anchoring, and sailing. Firstly, a multi-step preprocessing pipeline is established, incorporating trajectory cleaning, interpolation complementation, and segmentation to ensure data completeness and consistency; secondly, dynamic features—including speed, heading, and temporal progression—are encoded into an RGB three-channel image, which not only preserves the original spatial and temporal information of the trajectory but also strengthens the dimension of the feature expression of the image. Thirdly, the lightweight CNN architecture (CNN-Lite) is designed to automatically extract spatial motion patterns from these images, with data augmentation techniques further enhancing model robustness and generalization across diverse scenarios. Finally, comprehensive comparative experiments are conducted to evaluate the proposed method. On a real-world AIS dataset, the proposed method achieves an accuracy of 91.54%, precision of 91.51%, recall of 91.54%, and F1-score of 91.52%—demonstrating superior or highly competitive performance compared with SVM, KNN, MLSTM, ResNet-50 and Swin-Transformer in both classification accuracy and model stability. These results confirm that constructing dynamic-feature-enriched RGB trajectory images and designing a lightweight CNN can effectively improve ship behavior recognition performance and provide a practical and efficient technical solution for abnormal anchoring detection, maritime traffic monitoring, and development of intelligent shipping systems. Full article

With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development. Full article

Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model. Full article

A critical task in infrastructure security is to model user traffic in transportation systems to alert whenever anomalous behavior is observed. Discerning those abnormal samples is possible by auditing the available data, which then enables proper policy making to guarantee fair tariffs and the design of strategies to tackle problems such as passenger congestion. In this paper, we present an offline cybersecurity approach for the multimodal modeling of user traffic for the Colombian metro. To identify the anomalies, we design custom Deep Autoencoders based on the embeddings produced by the Self-Supervised Learning TabNet architecture. Additionally, we provide explainability through a SHAP-based component and the analysis of external image data using LLaVA as the selected Large Multimodal Model. The results indicate that most problems that occur on one metro line also affect the other, demonstrating the interconnectivity of the metro system, a crucial aspect that motivates the coordinated emergency response to improve the passenger travel experience. Although the detected problems might already have been identified and reported on social media, the transparency provided helps create confidence when an abnormality is observed, and in case there is no backup information on our official external data sources, it represents an alert to examine it more deeply, becoming an intelligent assessment tool for the metro. This article also sheds light on the potential of the publicly available dataset used and the importance of expanding its existing variables and information. Full article

Traffic anomaly detection plays a crucial role in improving road safety and enabling timely responses to abnormal events. Recent research has explored generative and predictive models to enhance detection accuracy; however, the dynamic and complex nature of traffic scenes often introduces noise and uncertainty, reducing reliability. This work presents TGU-Net, a Temporal Generative U-Net framework designed for real-time traffic anomaly detection in urban environments. The proposed model integrates two key innovations: (1) a temporal modeling component that captures dependencies across consecutive frames, and (2) contextual scene enrichment that enhances the distinction between normal and anomalous behaviors. These additions mitigate reconstruction noise and improve detection robustness without compromising computational efficiency. Experimental evaluations on a synthetically generated CARLA-based dataset demonstrate that TGU-Net achieves strong performance in precision, recall, and early anomaly detection, confirming its potential as a scalable and reliable framework for real-world traffic monitoring systems. Full article