Vehicular ad hoc networks (VANETs) are the communication foundation for future intelligent transportation systems and guarantee safe driving of intelligent networked vehicles. Moreover, VANETs face a series of security challenges related to the protection of vehicle privacy, authenticity of transmitted information, and bandwidth limitations. To resolve these contradictions, many certificateless aggregate signature (CLAS) schemes have been proposed. However, the majority of them can neither resist malicious-but-passive key generation center attacks, replay attacks, and link attacks, nor track the actual identities of malicious vehicles. Meanwhile, the security of previous CLAS schemes in VANETs is only formally provided in the random oracle model (ROM), which might be insecure in actual implementation. In addition, most CLAS schemes still have problems of large verification delays and high communication overhead. To address the above-mentioned problems, a new conditional privacy-preserving CLAS scheme in VANETs is proposed, which adopts full aggregation technology to reduce computation and bandwidth resources. According to the formal security proofs under the computational Diffie-Hellman problem (CDHP) given in the standard model (SM), the security level of this scheme is higher than that of other CLAS schemes under ROM in practical applications. Additionally, the use of pseudonym mechanism realizes conditional privacy protection in VANETs. The performance analysis shows that this scheme has a higher efficiency in terms of computation and communication overhead compared with several previous CLAS schemes.

Software-Defined Network (SDN) can improve the performance of the power communication network and better meet the control demand of the Smart Grid for its centralized management. Unfortunately, the SDN controller is vulnerable to many potential network attacks. The accurate detection of abnormal flow is especially important for the security and reliability of the Smart Grid. Prior works were designed based on traditional machine learning methods, such as Support Vector Machine and Naive Bayes. They are simple and shallow feature learning, with low accuracy for large and high-dimensional network flow. Recently, there have been several related works designed based on Long Short-Term Memory (LSTM), and they show excellent ability on network flow analysis. However, these methods cannot get the deep features from network flow, resulting in low accuracy. To address the above problems, we propose a Hybrid Convolutional Neural Network (HYBRID-CNN) method. Specifically, the HYBRID-CNN utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data. Finally, the proposed method is evaluated by experiments on the datasets of UNSW_NB15 and KDDCup 99. The experimental results show that the HYBRID-CNN significantly outperforms existing methods in terms of accuracy and False Positive Rate (FPR), which successfully demonstrates that it can effectively detect abnormal flow in the SDN-based Smart Grid.

To efficiently manage the cloud resources, improve the quality of service, and avoid the violations of Service-Level Agreement (SLA) agreements, it is very important to make accurate forecast for cloud workload. Prior works concerning cloud workload forecasting are mainly designed based on Recurrent Neural Networks (RNN). However, when it comes to a highly-dynamic cloud workload scenario where resource utilization changes faster and more frequently, these RNN-based methods are not effective in obtaining the linear and non-linear relationships and cannot give accurate forecast, because classic RNN has the problem of vanishing gradient. To address this issue, we propose an Ensemble Forecasting Approach (EFA) for highly-dynamic cloud workload by applying Variational Mode Decomposition (VMD) and R-Transformer. Specifically, to decrease the non-stationarity and high randomness of highly-dynamic cloud workload sequences, we decompose the workload into multiple Intrinsic Mode Functions (IMFs) by VMD. The IMFs are then imported into our ensemble forecasting module based on R-Transformer and Autoregressive model, in order to capture long-term dependencies and local non-linear relationship of workload sequences. The effectiveness and adaptability of proposed EFA is verified on real-world workload from Google and Alibaba cluster traces. Moreover, the performance evaluation results show that the EFA performs higher forecasting accuracy than prior related works over various forecasting time lengths for highly-dynamic cloud workload.