Abstract Anomaly detection in the Controller Area Network (CAN) bus is critical for ensuring the security and reliability of intelligent connected vehicles, which are increasingly prevalent. While existing anomaly detection strategies offer some benefits, they often face challenges such as limited feature extraction and data imbalance, which reduce their effectiveness. To address these issues, in this paper, we propose an unsupervised intrusion detection method based on CAN message graph named CGTS. Specifically, we first construct a message graph based on CAN message sequences. A Graph Transformer is then employed to extract complex structural information, accurately capturing the intrinsic connections between messages. Furthermore, to address the data imbalance problem, we integrate the Support Vector Data Description algorithm after the Graph Transformer model. This algorithm identifies anomalous behaviors efficiently without relying on a priori labels. Experiments conducted on public datasets, including Car-Hacking and CAN-Train-and-Test, demonstrate the efficacy of CGTS. The model achieves an average accuracy exceeding 0.990, precision above 0.995, and an F1-score nearing 0.993. These results highlight CGTS can effectively detect multiple injection attacks and significantly improve the CAN bus intrusion detection performance.

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of controller area network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system’s front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks.

This paper proposes Advanced Fast Recovery OLSR (AFR-OLSR) for Unmanned Aerial Vehicles (UAVs) with sudden link outages, and implements a multipath version of this protocol. AFR-OLSR can maintain high packet delivery ratio and hardly bring extra network delay in the scenario of nodes suddenly offline or moving at high speed in UAV swarm. We improve the route calculation rule by defining a new link state and using penalty function to reduce the priority of selecting the link state. We use a constant bit rate (CBR) application running on selected nodes to simulate traffic in the network and measure the performance of data transmission. Experimental results show that the proposed protocol has a significant performance improvement.

Multilevel thresholding is a widely used method in image segmentation. However, the traditional methods are costly to obtain the optimal thresholds through exhaustive search. The Nature-inspired algorithm is a gradient-free optimizer that overcomes these shortcomings and generates the best thresholds with high quality and efficiency. For this purpose, this paper suggests an improved arithmetic optimization algorithm with federated opposite learning for multilevel thresholding image segmentation, namely FOL-AOA. In this method, the federated opposite learning strategy is incorporated to avoid the particles being trapped into local optimal and increase the population diversity. The cross-entropy is employed as the objective function minimized by FOL-AOA. To assess the performance of the proposed method, we considered the use of a variety of benchmark images under different threshold levels and compared them against five predecessor approaches. The obtained results manifest that FOL-AOA outperforms the comparison methods in terms of the fitness values as well as two image quality indicators such as PSNR and SSIM.

Convolutional networks have been widely used for the classification of hyperspectral images; however, such networks are notorious for their large number of trainable parameters and high computational complexity. Additionally, traditional convolution-based methods are typically implemented as a simple cascade of a number of convolutions using a single-scale convolution kernel. In contrast, a lightweight multiscale convolutional network is proposed, capitalizing on feature extraction at multiple scales in parallel branches followed by feature fusion. In this approach, 2D depthwise convolution is used instead of conventional convolution in order to reduce network complexity without sacrificing classification accuracy. Furthermore, multiscale channel attention is also employed to selectively exploit discriminative capability across various channels. To do so, multiple 1D convolutions with varying kernel sizes provide channel attention at multiple scales, again with the goal of minimizing network complexity. Experimental results reveal that the proposed network not only outperforms other competing lightweight classifiers in terms of classification accuracy but also exhibits a lower number of parameters as well as significantly less computational cost.

Remote sensing image (RSI) scene classification plays an active role in many application areas. Due to the excellent performance of the convolutional neural networks (CNNs), which have widely applied in RSI scene classification in recent years. However, most existing methods improve the classification accuracy by improving the model parameters or fusing the features of CNNs. This will make the whole model very complicated and unable to extract multiscale features at a more granular level. This letter proposes a novel and lightweight multiscale depthwise network (MSDWNet) with efficient spatial pyramid attention (ESPA), namely ESPA-MSDWNet, with low model parameters and high accuracy in solving this problem. The ESPA-MSDWNet uses MobileNet V2 as a backbone. We represent multiscale features at a more granular level and expand the receptive fields by multiscale depthwise convolution (MSDW Conv). We also propose the ESPA module to extract dependencies between channels. The ablation experiment verifies the effectiveness of our proposed MSDW Conv and ESPA module. Experimental results on three public RSI datasets show that ESPA-MSDWNet has advantages in classification accuracy and execution efficiency over current state-of-the-art (SOTA) methods.

In remote sensing scene classification (RSSC), features can be extracted with different spatial frequencies where high-frequency features usually represent detailed information and low-frequency features usually represent global structures. However, it is challenging to extract meaningful semantic information for RSSC tasks by just utilizing high- or low-frequency features. The spatial composition of remote sensing images (RSIs) is more complex than that of natural images, and the scales of objects vary significantly. In this article, a multiscale feature fusion covariance network (MF2CNet) with octave convolution (Oct Conv) is proposed, which can extract multifrequency and multiscale features from RSIs. First, the multifrequency feature extraction (MFE) module is used to obtain fine-grained frequency features by Oct Conv. Then, the features of different layers in MF2CNet are fused by the multiscale feature fusion (MF2) module. Finally, instead of using global average pooling (GAP), global covariance pooling (GCP) extracts high-order information from RSIs to capture richer statistics of deep features. In the proposed MF2CNet, the obtained multifrequency and multiscale features can effectively improve the performance of CNNs. Experimental results on four public RSI datasets show that MF2CNet has advantages in RSSC over current state-of-the-art methods. The source codes of this method can be found athttps://github.com/liuqingxin-chd/MF2CNet.

The aluminum electrolytic capacitor (AEC) plays a key role in power electronic converter, but it is the most vulnerable component in the DC-DC converter. Throughout its short lifespan, the equivalent series resistance (ESR) will increase with the AEC aging. Therefore, it is important to monitor the ESR for ensuring the normal operation of a DC-DC converter. For a boost converter operating on discontinuous conduction mode and continuous conduction mode, an online noninvasive ESR estimation method was proposed in this paper. Based on the expression derivation of the output voltage, the ESR can be estimated by sampling the output ripple voltage at three specific moments. The proposed method is a noninvasive method, which does not require the current sensors. This method can be used in the discontinuous conduction mode and critical conduction mode. The simulation results verify the effectiveness of the proposed method.