Network Abnormal Data Detection Based on Deep Learning Model

To improve intrusion detection system performance,many algorithms are used to improve the performance of IDS systems, especially deep learning models. This paper presents an algorithm based on the model MLP, the training data set is the KDD99 data set, and the original data of the data set is vectorized by one-hot encoding, and the feature data is processed by Z-Score, and then the feature vector is encoded, and then the multi-layer perception is used The machine network performs feature learning, and finally trains the classifier model for detection. Traditional network anomaly detection algorithm models mainly use manual selection methods, and the accuracy and efficiency of classification problems are not high. This article first proposed the role of multilayer perceptron in Adam optimizer. The test of the KDD99 data set has been completed. The algorithm accuracy rate can reach 99%. For future network abnormal data detection work, an algorithm model that can realize real-time online detection is provided, which will have higher accuracy and better real-time performance.