A new network intrusion detection system based on auto-encoding and variance analysis

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. This work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. The experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods.