Treffer: IDMM-IDS: An efficient and robust intrusion detection system for the IoT based on the inverted Dirichlet mixture model.

Title:
IDMM-IDS: An efficient and robust intrusion detection system for the IoT based on the inverted Dirichlet mixture model.
Authors:
He W; College of Computer Science, TKLNDST, Nankai University, Tianjin, China., Cai X; College of Computer Science, TKLNDST, Nankai University, Tianjin, China. Electronic address: caixr@nankai.edu.cn., Yu Y; School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China., Lai Y; School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China., Yuan X; College of Computer Science, TKLNDST, Nankai University, Tianjin, China.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jan; Vol. 193, pp. 108002. Date of Electronic Publication: 2025 Aug 18.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Internet of Things* , Computer Security* , Neural Networks, Computer*, Algorithms ; Humans ; Stochastic Processes
Contributed Indexing:
Keywords: Bayesian inference; Class imbalance; Extended stochastic variational inference; Internet of Things (IoT); Intrusion detection system
Entry Date(s):
Date Created: 20250828 Date Completed: 20251217 Latest Revision: 20251217
Update Code:
20251217
DOI:
10.1016/j.neunet.2025.108002
PMID:
40876297
Database:
MEDLINE

Weitere Informationen

The Internet of Things (IoT) has permeated all facets of modern life, offering revolutionary applications from smart homes to industrial automation. However, the widespread adoption of IoT systems has amplified security vulnerabilities, necessitating robust intrusion detection systems (IDSs) to protect these devices. Traditional IDS solutions often face challenges in resource-constrained IoT environments due to high computational demands and limited adaptability to emerging threats. To address these issues, this paper proposes IDMM-IDS, an efficient and robust IDS tailored for IoT contexts. By utilizing the inverted Dirichlet mixture model (IDMM) and extended stochastic variational inference (ESVI), our IDMM-IDS models complex network traffic with minimal computational overhead. Additionally, a novel cluster-based oversampling technique is integrated to address class imbalance, enhancing the detection of minority class threats without introducing noise. Extensive evaluations on three public datasets-UNSW-NB15, WSN-DS, and WUSTL-IIOT-2021-demonstrate that IDMM-IDS outperforms most existing methods in detection performance while significantly reducing training and decision times, making it well-suited for resource-constrained IoT environments.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.