Soft multimedia anomaly detection based on neural network and optimization driven support vector machine

Software multimedia anomaly detection model based on neural network and optimization driven support vector machine is discussed in this paper. For multimedia information, most traditional information security technology has its limitations. For example, the limitation of the encryption technology is that on the one hand, the encrypted files resulting from the incomprehension of attributes interfere with the transfer of multimedia information. On the other hand, the encrypted multimedia information is likely to attract the attacker's curiosity and attention, and is likely to be cracked, and once it is cracked, the system loses control of the information. To deal with these challenges, this study integrates soft computing techniques to finalize the enhanced multimedia anomaly detection model. With respect to the neural network, a random system with random factors is referred to as a random system. These practical systems are generally described and modeled by stochastic differential equations. In this study, we combined the double support vector machine and decision tree support vector machine to construct a new double support vector machine decision tree classifier. Kernel function and convex optimization were integrated to guarantee an optimal solution. Experimental results demonstrated the robustness of the model compared with other recent techniques.