The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and...The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.展开更多
Data hiding plays an important role in privacy protection and authentication,but most data hiding methods fail to achieve satisfactory performance in resisting scaling attacks and additive attacks.To this end,this pap...Data hiding plays an important role in privacy protection and authentication,but most data hiding methods fail to achieve satisfactory performance in resisting scaling attacks and additive attacks.To this end,this paper proposes a new quantization index modulation(QIM)variant based on division domains(D-QIM).It can not only resist the above two attacks well,but also adjust the performance trade-offs by controlling the parameters.Simulation results confirm the performance gain of D-QIM in terms of the bit error rate(BER).展开更多
基金supported in part by the Open Research Fund of Joint Laboratory on Cyberspace Security,China Southern Power Grid(Grant No.CSS2022KF03)the Science and Technology Planning Project of Guangzhou,China(GrantNo.202201010388)the Fundamental Research Funds for the Central Universities.
文摘The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.
基金supported in part by the National Natural Science Foundation of China (61902149,61932010 and 62032009)the Natural Science Foundation of Guangdong Province (2020A1515010393).
文摘Data hiding plays an important role in privacy protection and authentication,but most data hiding methods fail to achieve satisfactory performance in resisting scaling attacks and additive attacks.To this end,this paper proposes a new quantization index modulation(QIM)variant based on division domains(D-QIM).It can not only resist the above two attacks well,but also adjust the performance trade-offs by controlling the parameters.Simulation results confirm the performance gain of D-QIM in terms of the bit error rate(BER).
