An Intrusion Detection Method Based on Transfer Component Analysis and an Extreme Learning Machine

Intrusion detection technology is an active network defense technology that has quickly become an important means of ensuring network security since it was proposed. With the development of machine learning, its application in intrusion detection has effectively improved the detection effect. However, due to the increasing volume and dimension of the current network data, the model training speed of machine learning-based intrusion detection technology is slow, and the update cost is high. In addition, different distributions of intrusion detection data affect the detection effect. In this paper, the extreme learning machine (ELM) model is extended. An intrusion detection method, TCA-ELM, based on transfer component analysis (TCA) and an extreme learning machine is proposed. TCA-ELM utilizes TCA to extract data features and project data to a new space by greatly reducing the distance and dimension between the data distributions and then trains the ELM in the new feature space to improve the training efficiency and detection rate. Experimental tests are performed on the famous NSL-KDD dataset, and the experimental results demonstrate the effectiveness of the proposed TCA-ELM method. Compared with the other four benchmark algorithms, the training time is significantly improved, and the detection accuracy is also significantly improved.