The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic engineering in intelligent transportation systems. Accurate forecast results can assist in traffic management and pedestrian route selection, which will help alleviate the huge congestion problem in the system. This paper presents a novel hybrid DTMGP model to accurately forecast the volume of passenger flows multi-step ahead with the comprehensive consideration of factors from temporal, origin-destination spatial, and frequency and self-similarity perspectives. We first apply discrete wavelet transform to decompose the traffic volume series into an appropriation component and several detailed components. Then we propose a more efficient tracking model to forecast the appropriation component and a novel Gaussian process model to forecast the detailed components. The forecasting performance is evaluated with real-time passenger flow data in Chongqing, China. Simulation results demonstrate that our hybrid model can achieve on average 20%-50% accuracy improvement, especially during rush hours.

Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time and record huge volumes of traffic data. Exploiting such data to forecast future traffic conditions have the potential to enhance the decision-making capabilities of intelligent transportation systems, which attracts widespread attention from both industries and academia. Among them, network-wide prediction based on graph convolutional neural networks(GCN) has become mainstream. It models the spatial dependencies of sensors in a graph with a pre-defined Laplacian matrix based on the distances among sensors. However, understanding spatio-temporal traffic patterns is quite challenging as there is a huge difference in terms of traffic patterns during different periods or in different regions. In addition, the actual data collected can be polluted due to unavoidable data loss from severe communication conditions or sensor failures. Considering these issues, we propose a novel dynamic multiview spatial-temporal prediction framework which takes into consideration various factors, including local/global, short/long term spatio-temporal dependencies and their dynamic changes. To comprehensively track the dynamic spatio-temporal dependencies among traffic data, we creatively design two different modules to perceive the changes in traffic patterns. We first propose a dynamic Laplacian matrix learning module based on our theoretical derivation to estimate the Laplacian matrix of the graph for GCN timely. We creatively incorporate tensor decomposition into this module, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationships and a local component that captures the traffic fluctuations. We also design a self-attention based module to dynamically assign a weight to each part in traffic data. The spatio-temporal features from multiple views are deeply fused by a feature fusion module. The forecasting performance is evaluated with 5 real-time traffic datasets. Experiment results demonstrate that our framework can consistently outperform the state-of-the-art baselines and be more robust under noisy environments.

The rapid expansion of the Internet of Things (IoT) has led to growing concerns about the security of IoT devices. A crucial aspect of ensuring their security is IoT device identification, which involves pinpointing the specific type of device. Existing solutions, however, either necessitate complex feature engineering or struggle to handle the ever-increasing number of new devices in open IoT environments. To tackle these challenges, this paper introduces GraphIoT, a lightweight IoT device detection method based on graph classifiers. GraphIoT leverages lightweight flow information, such as packet length, direction, and timestamp, to create an IoT Device Traffic Graph Representation (IoT-DTGR). This representation offers a comprehensive view of IoT device flows while preserving features in bidirectional IoT Device-Gateway interactions. By transforming the IoT device detection problem into a graph classification problem, GraphIoT employs a powerful Graph Neural Network that takes into account both node and edge features, as well as subgraph structures in IoT-DTGRs, to classify graphs and consequently identify device types. Additionally, the paper proposes an incremental learning framework called CL-GraphIoT that continuously learns features of new IoT device flows without forgetting previously learned device features. This is achieved through two strategies: parameter sharing and sample replaying. The paper gathers a real-world dataset from 18 IoT devices and conducts experiments on two datasets: the gathered real-world dataset and an open-source dataset covering 21 IoT device types. The experimental results demonstrate that both GraphIoT and CL-GraphIoT outperform state-of-the-art methods, achieving high accuracy in device detection with fast processing speed.

Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately detecting the abnormality of MTS is very critical for subsequent fault elimination. The scarcity of anomalies and manual labeling has led to the development of various self-supervised MTS anomaly detection (AD) methods, which optimize an overall objective/loss encompassing all metrics' regression objectives/losses. However, our empirical study uncovers the prevalence of conflicts among metrics' regression objectives, causing MTS models to grapple with different losses. This critical aspect significantly impacts detection performance but has been overlooked in existing approaches. To address this problem, by mimicking the design of multi-gate mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI Anomaly Detection algorithm. CAD offers an exclusive structure for each metric to mitigate potential conflicts while fostering inter-metric promotions. Upon thorough investigation, we find that the poor performance of vanilla MMoE mainly comes from the input-output misalignment settings of MTS formulation and convergence issues arising from expansive tasks. To address these challenges, we propose a straightforward yet effective task-oriented metric selection and p&s (personalized and shared) gating mechanism, which establishes CAD as the first practicable multi-task learning (MTL) based MTS AD model. Evaluations on multiple public datasets reveal that CAD obtains an average F1-score of 0.943 across three public datasets, notably outperforming state-of-the-art methods. Our code is accessible at https://github.com/dawnvince/MTS_CAD.

Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.