Biblio

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.

For the security enhancement of the conventional visible light (VL) communication which allows the easy intrusion by adjacent adversary due to visible signal characteristic, the VL communication technique based on the asymmetric Rivest-Shamir-Adleman (RSA) encryption method is proposed for smart indoor service in this paper, and the optimal key length of the RSA encryption process for secure VL communication technique is investigated, and also the error performance dependent on the various asymmetric encryption key is analyzed for the performance evaluation of the proposed technique. Then we could see that the VL communication technique based on the RSA encryption gives the similar RMSE performance independent of the length of the public or private key and provides the better error performance as the signal to noise ratio (SNR) increases.

In order to investigate the relationship and effect on the performance of magnetic modulator among applied DC current, excitation source, excitation loop current, sensitivity and induced voltage of detecting winding, this paper measured initial permeability, maximum permeability, saturation magnetic induction intensity, remanent magnetic induction intensity, coercivity, saturated magnetic field intensity, magnetization curve, permeability curve and hysteresis loop of main core 1J85 permalloy of magnetic modulator based on ballistic method. On this foundation, employ curve fitting tool of MATLAB; adopt multiple regression method to comprehensively compare and analyze the sum of squares due to error (SSE), coefficient of determination (R-square), degree-of-freedom adjusted coefficient of determination (Adjusted R-square), and root mean squared error (RMSE) of fitting results. Finally, establish B-H curve mathematical model based on the sum of arc-hyperbolic sine function and polynomial.

Recommender system is to suggest items that might be interest of the users in social networks. Collaborative filtering is an approach that works based on similarity and recommends items liked by other similar users. Trust model adopts users' trust network in place of similarity. Multi-faceted trust model considers multiple and heterogeneous trust relationship among the users and recommend items based on rating exist in the network of trustees of a specific facet. This paper applies genetic algorithm to estimate parameters of multi-faceted trust model, in which the trust weights are calculated based on the ratings and the trust network for each facet, separately. The model was built on Epinions data set that includes consumers' opinion, rating for items and the web of trust network. It was used to predict users' rating for items in different facets and root mean squared of prediction error (RMSE) was considered as a measure of performance. Empirical evaluations demonstrated that multi-facet models improve performance of the recommender system.