Open Access

ARTICLE

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

Hanqing Sun¹, Xue Li^2,*, Qiyuan Fan³, Puming Wang³

1 School of Information Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, 450046, China
2 School of Electronic Information Engineering, Henan Institute of Technology, Xinxiang, 453002, China
3 School of Software, Yunnan University, Kunming, 650500, China

* Corresponding Author: Xue Li. Email:

(This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)

Computers, Materials & Continua 2025, 84(1), 1659-1679. https://doi.org/10.32604/cmc.2025.061426

Received 24 November 2024; Accepted 06 May 2025; Issue published 09 June 2025

Abstract

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.

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Keywords

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