Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies...Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.展开更多
The integration of cyber(network)with physical world is a big step in cyber-physical systems.This has revolutionised many industries.But this transformation has made cyber-physical sys-tems vulnerable to attacks.One p...The integration of cyber(network)with physical world is a big step in cyber-physical systems.This has revolutionised many industries.But this transformation has made cyber-physical sys-tems vulnerable to attacks.One particular type of attack is the adversarial false data injection which injects false data in either the sensor measurements or the corresponding communica-tion channel.A better understanding of how false data injection(FDI)attacks are constructed is crucial for developing strategies to protect against such attacks.In this paper,we consider two models for networked control systems and present an algorithm for constructing FDI attacks in each case and compare with an existing approach.The conditions for the attack to remain stealthy for systems equipped with aχ^(2) failure detector and the design of attack vectors that satisfy these conditions are discussed in detail.The algorithms are demonstrated by developing FDI attacks for two real-world examples.展开更多
文摘Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.
文摘The integration of cyber(network)with physical world is a big step in cyber-physical systems.This has revolutionised many industries.But this transformation has made cyber-physical sys-tems vulnerable to attacks.One particular type of attack is the adversarial false data injection which injects false data in either the sensor measurements or the corresponding communica-tion channel.A better understanding of how false data injection(FDI)attacks are constructed is crucial for developing strategies to protect against such attacks.In this paper,we consider two models for networked control systems and present an algorithm for constructing FDI attacks in each case and compare with an existing approach.The conditions for the attack to remain stealthy for systems equipped with aχ^(2) failure detector and the design of attack vectors that satisfy these conditions are discussed in detail.The algorithms are demonstrated by developing FDI attacks for two real-world examples.
