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Cited by 14Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingNetwork Packet Processing and Optimization

Enhancing trustworthiness among iot network nodes with ensemble deep learning-based cyber attack detection

Malathi, Dr S, Begum, S. Razool·2024·Expert Systems with Applications
DOI 10.1016/j.eswa.2024.124528
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  • In communication-centric attack scenarios, coarse-grained traffic statistics and entropy measures are sufficient to expose abnormal behavior without requiring deep protocol parsing.

    “In these scenarios, coarse-grained traffic statistics and entropy measures are sufficient to expose abnormal behavior without requiring deep protocol parsing [13,21,28,32].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [13]

  • Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available, whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy.

    “Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available [13,15,17,20,23,25,32,34], whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy [17,20,21,25,28,34].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [13]

  • Deep architectures such as DAE, DAGMM, GCNN, and LSTM-based hybrids are mainly beneficial when modeling nonlinear feature interactions or temporal dependencies, but they can be sensitive to preprocessing choices and class imbalance, sometimes exhibiting complementary false-positive/false-negative patterns compared with simpler models.

    “Deep architectures such as DAE, DAGMM, GCNN, and LSTM-based hybrids are mainly beneficial when modeling nonlinear feature interactions or temporal dependencies [13,15,17,21,23,29], but they can be sensitive to preprocessing choices and class imbalance, sometimes exhibiting complementary false-positive/falsenegative patterns compared with simpler models [20,21,28].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [13]

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Authors
Malathi, D. S., & Begum, S. R.
Affiliations
Bharathidasan University (IN)
Corresponding author
Begum, S. Razool
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2024-12
Volume
255
Pages
124528
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2024.124528
OpenAlex
W4399850328
Cited by
14
References
34
Citations by year
2026: 2 · 2025: 11 · 2024: 1
FWCI
2.34
Type
journal article
Language
en
Open access
closed
Licence
Locations
Expert Systems with Applications (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
  • Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
Keywords
cyberattack detection, Internet of Things, cyber-physical systems, network intrusion detection, network traffic classification, feature selection, graph convolutional networks, ensemble learning, XGBoost, random trees, IoT-23 dataset, critical infrastructure security

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