Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enha...Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.展开更多
The low-rate denial of service attack is more applicable to the network in recent years as a means of attack, which is different from the traditional field type DoS attacks at the network end system or network using a...The low-rate denial of service attack is more applicable to the network in recent years as a means of attack, which is different from the traditional field type DoS attacks at the network end system or network using adaptive mechanisms exist loopholes flow through the low-rate periodic attacks on the implementation of high-efficiency attacked by an intruder and not be found, resulting in loss of user data or a computer deadlock. LDos attack since there has been extensive attention of researchers, the attack signature analysis and detection methods to prevent network security have become an important research topic. Some have been proposed for the current attacks were classified LDoS describe and model, and then in NS-2 platform for experimental verification, and then LDoS attack detection to prevent difficulties are discussed and summarized for the future such attacks detection method research work to provide a reference.展开更多
Traceability precept is a broadcast encryption technique that content suppliers can trace malicious authorized users who leak the decryption key to an unauthorized user. To protect the data from eavesdropping, the con...Traceability precept is a broadcast encryption technique that content suppliers can trace malicious authorized users who leak the decryption key to an unauthorized user. To protect the data from eavesdropping, the content supplier encrypts the data and broadcast the cryptograph that only its subscribers can decrypt. However, a traitor may clone his decoder and sell the pirate decoders for profits. The traitor can modify the private key and the decryption program inside the pirate decoder to avoid divulging his identity. Furthermore, some traitors may fabricate a new legal private key together that cannot be traced to the creators. So in this paper, a renewed precept is proposed to achieve both revocation at a different level of capacity in each distribution and black-box tracing against self-protective pirate decoders. The rigorous mathematical deduction shows that our algorithm possess security property.展开更多
文摘Smart grid systems are advancing electrical services,making them more compatible with Internet of Things(IoT)technologies.The deployment of smart grids is facing many difficulties,requiring immediate solutions to enhance their practicality.Data privacy and security are widely discussed,and many solutions are proposed in this area.Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks.The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems,which is commonly solved via fog computing.This work proposes an energytheft detection method for smart grid systems employed in a fog-based network infrastructure.This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach.The detection process occurs at fog nodes via five machine-learning classification models.The performance of the classifiers is measured,validated,and reported for all models at fog nodes,as well as the required training and testing time.Finally,the measured results are compared to when the detection process occurs at a central processing unit(cloud server)to investigate and compare the performance metrics’goodness.The results show comparable accuracy,precision,recall,and F1-measure performance.Meanwhile,the measured execution time has decreased significantly in the case of the fog-based network infrastructure.The fog-based model achieved an accuracy and recall of 98%,F1 score of 99%,and reduced detection time up to around 85%compared to the cloud-based approach.
文摘The low-rate denial of service attack is more applicable to the network in recent years as a means of attack, which is different from the traditional field type DoS attacks at the network end system or network using adaptive mechanisms exist loopholes flow through the low-rate periodic attacks on the implementation of high-efficiency attacked by an intruder and not be found, resulting in loss of user data or a computer deadlock. LDos attack since there has been extensive attention of researchers, the attack signature analysis and detection methods to prevent network security have become an important research topic. Some have been proposed for the current attacks were classified LDoS describe and model, and then in NS-2 platform for experimental verification, and then LDoS attack detection to prevent difficulties are discussed and summarized for the future such attacks detection method research work to provide a reference.
基金This work was supported by the Large-Scale Security SoC Project of Wuhan Science and Technology Bureau of China under Grand No. 20061005119.
文摘Traceability precept is a broadcast encryption technique that content suppliers can trace malicious authorized users who leak the decryption key to an unauthorized user. To protect the data from eavesdropping, the content supplier encrypts the data and broadcast the cryptograph that only its subscribers can decrypt. However, a traitor may clone his decoder and sell the pirate decoders for profits. The traitor can modify the private key and the decryption program inside the pirate decoder to avoid divulging his identity. Furthermore, some traitors may fabricate a new legal private key together that cannot be traced to the creators. So in this paper, a renewed precept is proposed to achieve both revocation at a different level of capacity in each distribution and black-box tracing against self-protective pirate decoders. The rigorous mathematical deduction shows that our algorithm possess security property.
