Abstract
Today, challenges such as a high false-positive rate, a low detection rate, a slow processing speed, and a big feature dimension are all part of intrusion detection. To address these issues, decision trees (DTs), deep neural networks (DNNs), and principal component analysis (PCA) are available. Through a higher detection rate and a lower false-positive rate, the research-based intrusion detection model DT-PCA-DNN increases the processing speed of intrusion detection systems (IDSs). To minimize the overall data volume and accelerate processing, DT is used to initially differentiate the data. Differentiate DTs save the temporary training sample set for intrusion data in order to retrain and optimize the DT and DNN, treat the DT judges as standard data, and delete the added average data. After signing, we should lower the dimension of the data using PCA and then submit the data to DNN for secondary discrimination. However, DT employs a shallow structure in order to prevent an excessive quantity of average numbers from being interpreted as intrusion data. As a result, additional DNN secondary processing cannot effectively increase the accuracy. DNN accelerates data processing by utilizing the ReLU activation function from the simplified neural network calculation approach and the faster convergence ADAM optimization algorithm. Class two and five trials on the NSL-KDD dataset demonstrate that the proposed model is capable of achieving high detection accuracy when compared to other deep learning-based intrusion detection approaches. Simultaneously, it has a faster detection rate, which effectively solves the real-time intrusion detection problem.