Biblio

Based on the characteristics of the new power system with many points, wide range and unattended, this paper studies the specific Cyberspace security risks faced by the disease control side, the station side and the site side, and proposes a new power system Cyberspace security assurance system of “integration of collection, network, side, end, industry and people”. The site side security access measures, the site side civil air defense technology integration measures, the whole business endogenous security mechanism, the whole domain communication security mechanism, the integrated monitoring and early warning and emergency response mechanism are specifically adopted to form a comprehensive integrated security mechanism for the new power system, form a sustainable protection model, effectively improve the security capability, while taking into account the cost and operational complexity of specific implementation links, Provide comprehensive guarantee capability for the safe operation of the new power system.

This study shows the effectiveness of anomaly-based IDS using long short-term memory(LSTM) based on the newly developed dataset called UNSW-NB15 while considering root mean square error and mean absolute error as evaluation metrics for accuracy. For each attack, 80% and 90% of samples were used as LSTM inputs and trained this model while increasing epoch values. Furthermore, this model has predicted attack points by applying test data and produced possible attack points for each attack at the 3rd time frame against the actual attack point. However, in the case of an Exploit attack, the consecutive overlapping attacks happen, there was ambiguity in the interpretation of the numerical values calculated by the LSTM. We presented a methodology for training data with binary values using LSTM and evaluation with RMSE metrics throughout this study.

With the proliferation of false redundant information on various e-commerce platforms, ineffective recommendations and other untrustworthy behaviors have seriously hindered the healthy development of e-commerce platforms. Modern recommendation systems often use side information to alleviate these problems and also increase prediction accuracy. One such piece of side information, which has been widely investigated, is trust. However, it is difficult to obtain explicit trust relationship data, so researchers infer trust values from other methods, such as the user-to-item relationship. In this paper, addressing the problems, we proposed a novel trust-based recommender model called UITrust, which uses user-item relationship value to improve prediction accuracy. With the improvement the traditional similarity measures by employing the entropies of user and item history ratings to reflect the global rating behavior on both. We evaluate the proposed model using two real-world datasets. The proposed model performs significantly better than the baseline methods. Also, we can use the UITrust to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

This paper studies the hierarchical secure multicast algorithm in sparse code multiple access (SCMA) networks, its network security capacity is no longer limited by the users with the worst channel quality in multicast group. Firstly, we propose a network security energy efficiency (SEE) maximization problem. Secondly, in order to reduce the computational complexity, we propose a suboptimal algorithm (SA), which separates the codebook assignment with artificial noise from the power allocation with artificial noise. To further decrease the complexity of Lagrange method, a power allocation algorithm with increased fixed power is introduced. Finally, simulation results show that the network performance of the proposed algorithm in SCMA network is significantly better than that in orthogonal frequency division multiple access (OFDMA) network.

In Particle Swarm Optimization Algorithm (PSO), the learning factors \$c\_1\$ and \$c\_2\$ are used to update the speed and location of a particle. However, the setting of those two important parameters has great effect on the performance of the PSO algorithm, which has limited its range of applications. To avoid the tedious parameter tuning, we introduce a transfer learning based adaptive parameter setting strategy to PSO in this paper. The proposed transfer learning strategy can adjust the two learning factors more effectively according to the environment change. The performance of the proposed algorithm is tested on sets of widely-used benchmark multi-objective test problems for DTLZ. The results comparing and analysis are conduced by comparing it with the state-of-art evolutionary multi-objective optimization algorithm NSGA-III to verify the effectiveness and efficiency of the proposed method.