As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metamorphism mechanisms used by malware developers and how to effectively address them. This work presents an innovative Artificial Evolutionary Fuzzy LSTM Immune System which, by using a heuristic machine learning method that combines evolutionary intelligence, Long-Short-Term Memory (LSTM), and fuzzy knowledge, proves to be able to adequately protect modern information system from Portable Executable Malware. The main innovation in the technical implementation of the proposed approach is the fact that the machine learning system can only be trained from raw bytes of an executable file to determine if the file is malicious. The performance of the proposed system was tested on a sophisticated dataset of high complexity, which emerged after extensive research on PE malware that offered us a realistic representation of their operating states. The high accuracy of the developed model significantly supports the validity of the proposed method. The final evaluation was carried out with in-depth comparisons to corresponding machine learning algorithms and it has revealed the superiority of the proposed immune system.

This paper deals with the state estimation fusion problem for stochastic continuous-time systems over wireless sensor networks (WSNs) with multi-sensor scheduling. Due to the power limitation and communication constraints in WSNs, a closed-loop stochastic event-triggered mechanism is designed to reduce the redundant data transmission. Since this design preserves the Gaussian property of the conditional distribution of the system state, an exact minimum mean square error (MMSE) estimator could be presented for every sensor subsystem. Then, the distributed event-triggered information fusion estimator is proposed based on maximum a posterior probability criterion by a matrix-weighted combination of all available local estimates from sensor subsystems. The proposed distributed algorithm has advantages on reliability, computation efficiency due to the netted parallel structure. A numerical simulation is conducted to illustrate the effectiveness of the proposed distributed estimator.

The determination of the spatial heterogeneity of the regional evapotranspiration over a complex underlying surface in an oasis-desert region is crucial for water resource management in a river basin and aiding in irrigation decisions. The surface energy balance system (SEBS) model has been widely used to estimate surface energy fluxes. However, the parameterization of surface roughness length for momentum transfer (z0m) and heat transfer (z0h) did not perform well for a complex underlying surface. Moreover, it is difficult to estimate surface soil heat flux, i.e., G0, accurately at the regional scale. In this letter, the parameterization schemes of z0m, z0h, and G0were optimized. Measurements from 21 sets of eddy covariance systems were used to validate the model performance. The results show that the revised SEBS model root-mean-square errors (RMSEs) of the satellite-based sensible and latent heat fluxes (H and LE) decreased from 97.2 W · m-2to 56.9 W · m-2and from 102.9 W · m-2to 74.8 W · m-2, respectively, at the footprint scale. At the pixel scale, the RMSEs of the revised model estimates of the H and LE were 40.9 W · m-2and 57.5 W · m-2, respectively. The improved agreements between the estimates and the measurements indicate that the revised SEBS model is appropriate for estimating regional energy fluxes over heterogeneous oasis-desert surfaces. Furthermore, the spatial and temporal patterns of the LE in the middle reaches of the Heihe River were investigated.

This paper proposes an image binarization method based on the human vision system. It firstly gets the image threshold value according to the Euclidean distance, then uses the standard deviation to determine the visual sensitivity coefficient of each pixel according to the human visual characteristic, finally performs image binarization according to the gray value, the image threshold and the visual sensitivity coefficient. This method can both effectively eliminate the ghosts, and keep the character stroke well. The experimental results show that this method is more effective than Ostu method and Bernsen method. It is superior to Ostu method and Bernsen method in the entropy. It also provides a new effective way for image binarization.