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ETFIDS: An Entropy-Driven, Time-Frequency Analysis Framework for In-Vehicle CAN Signal Intrusion Detection

IEEE Internet of Things Journal 2025年

作者： Liu, Wanning Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Qingxin Zhou, Xue Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Changchun130012 China

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. © 2025 IEEE.

关键词： Network intrusion

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Effect of Mn alloying on the hydrogen-assisted cracking behavior in multiphase/duplex stainless steel

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材料科学技术（英文版） 2025年第4期207卷 126-141页

作者： Menghao Liu Cuiwei Du Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China Key Laboratory for Corrosion and Protection of the Ministry of Education Institute of Advanced Materials & TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China

There remains debate on whether Mn is beneficial or detrimental to hydrogen embrittlement in stainless *** this work,a series of stainless steels were designed to study the change of hydrogen embrit-tlement sensitivity,crack propagation,and hydrogen trapping behaviors upon Mn *** results suggest that adding 4 wt.％Mn increased hydrogen embrittlement susceptibility,whereas adding 8 wt.％Mn decreased hydrogen embrittlement *** banded α'-martensite through austenitic grain is the main reason for the increased hydrogen embrittlement sensitivity when adding 4 wt.％Mn,by ad-sorbing hydrogen,promoting crack initiation,and accelerating crack propagation.

关键词： Hydrogen-assisted cracking Multiphase stainless steel Manganese

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Research and Implementation of OFC-Based Multicarrier Sliceable Bandwidth Variable Transponders in Hybrid Laser/Microwave Satellite Networks

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IEEE Access 2025年 13卷 93046-93056页

作者： Bin Wu Hao Zhang Qingchun Zhao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

In this paper, a multicarrier sliceable bandwidth variable transponder based on optical frequency comb (OFC) is proposed. The structure of the transponder’s internal switching unit is designed, and the principle of generating ultra-flat OFC based on a standalone dual-parallel Mach-Zehnder modulator (DPMZM) is analyzed. A scheme for dynamic allocation of multi-channel frequency slots and flexible resource configuration in hybrid laser/satellite networks is provided, and an experimental system is built to test the data transmission and bandwidth allocation capabilities of laser and microwave links. Experimental results indicate that the muticarrier generation unit can produce a 7-line flat OFC with a flatness of 1.6 dB, and the switching unit can achieve channel switching and frequency slot allocation, with a minimum slice width of 12.5 GHz. After passing through this node, the phase noise of the 2 GHz IF signal with frequency offsets at 10 kHz and 100 kHz are −59.28 dBc/Hz and −87.95 dBc/Hz, respectively, and the bit error rates (BERs) of the recovered baseband data from the laser and microwave links are less than $10^{-9}$ . These results validate the feasibility of the designed transponder to be applied in the key relay nodes of satellite networks. The proposed scheme can solve the problems of low spectrum utilization and the inability to dynamically allocate resources in traditional WDM satellite networks.

关键词： Satellites Transponders Masers Optical switches Frequency modulation Bandwidth Resource management Optical filters Microwave communication Optical fiber communication

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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An Area Optimization Approach for Large-Scale RM-TB Dual Logic Circuits Based on a Multitasking Optimization Algorithm

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IEEE Transactions on Computers 2025年

作者： Wu, Xiaoqian Wang, Peng Li, Shaoquan Liu, Huaxiao Liu, Lei Jilin University College of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation China Knowledge Engineering of Ministry of Education China

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach. © 1968-2012 IEEE.

关键词： Combinatorial optimization

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Calibration of Car-Following Behavior Based on Monocular Camera 11th

Calibration of Car-Following Behavior Based on Monocular Cam...

引用

11th International Conference on Traffic and Transportation Studies, ICTTS 2024

作者： Liu, Yufei Wei, Chong MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Mechanical and Electrical Engineering Hainan University Haikou China

ISBN: (纸本)9789819796434

In the past, most of the research on car-following models in China focused on physical statistics, with relatively little collection and analysis of car-following data. Therefore, it is essential to establish a car-following model suitable for analyzing the car-following characteristics of Chinese drivers based on real driving data. This paper utilizes a vehicle-mounted monocular camera to capture traffic flow images on the highway and uses the YOLOv5 algorithm to detect vehicles. Through the homography transformation between the image plane in the front view and the spatial plane in the top view, the pixel coordinates are converted to 3D spatial coordinates. Then, the ranging model is established, and its effectiveness is verified using the length of the lane dividing lines. The factor analysis is used to determine the main influencing factors of car-following behavior. The appropriate linear car-following models are selected and its parameters are calibrated using multiple linear regression methods. This paper realizes the collection and subsequent processing of car-following behavior data under natural driving conditions, and transforms generalized car-following models into specific research theories for application in practical situations. This method of collecting car-following driving data has strong practicality and can provide effective data support for studying car-following models that accurately reflect the operation conditions of road traffic. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Behavioral research

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arXiv

引用

arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A Revisit of Landsat-Observed Macroalgal Blooms in the Yellow Sea and East China Sea Before 2008

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Journal of Ocean University of China 2025年第2期24卷 525-531页

作者： YUAN Chao HUANG Juan WU Lingjuan XIAO Jie ZHANG Xuelei GAO Song LI Yifei XU Jiangling WANG Zongling Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation North China Sea Marine Forecasting and Hazard Mitigation CenterMinistry of Natural ResourcesQingdao 266061China Laboratory for Marine Ecology and Environmental Science Qingdao Marine Science and Technology CenterQingdao 266061China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China

Harmful macroalgal blooms caused by Ulva prolifera and Sargassum horneri are increasing in the Yellow Sea and East China ***,our understanding of macroalgal blooms before 2008 is fragmented and *** time-series Landsat imagery from 1984 to 2008,we examined macroalgal bloom events and their evolutionary *** results suggest that no macroalgal blooms were observed before *** blooms could be traced back to 1999 and occurred on a small scale in 2000,2004 and 2005,before escalating into large green tides in 2007 and ***,these Ulva blooms were confined to the southern Yellow Sea from May to *** comparison,Sargassum blooms were first detected in the East China Sea in March 2000 and occurred almost every year thereafter,although the size of the blooms showed significant interannual *** distribution areas generally moved northwards from March until the bloom dissipated in May or June,suggesting the influence of the monsoon and *** investigation provided some insight into the bloom history of these two harmful macroalgal blooms in the Yellow Sea and East China Sea.

关键词： macroalgal bloom green tide golden tide remote sensing Landsat Yellow Sea East China Sea

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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