TIDS: Tensor Based Intrusion Detection System (IDS) and Its Application in Large Scale DDoS Attack Detection

The era of big data brings new challenges for information network systems (INS), simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems. In this work, we propose a data-driven intrusion detection system for Distributed Denial of Service (DDoS) attack detection. The system focuses on intrusion detection from a big data perceptive. As intelligent information processing methods, big data and artificial intelligence have been widely used in information systems. The INS system is an important information system in cyberspace. In advanced INS systems, the network architectures have become more complex. And the smart devices in INS systems collect a large scale of network data. How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge. To address the problem, we design a novel intrusion detection system (IDS) from a big data perspective. The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor. Then, a novel tensor decomposition (TD) method is developed to complete big data mining. The TD method seamlessly collaborates with the XGBoost (eXtreme Gradient Boosting) method to complete the intrusion detection. To verify the proposed IDS system, a series of experiments is conducted on two real network datasets. The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%. Additionally, by altering the scale of the datasets, the proposed IDS system still maintains excellent detection performance, which demonstrates the proposed IDS system’s robustness.