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Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 August 2026) | Viewed by 7708

Editor

Dr. Emre Tokgoz

E-Mail Website
Guest Editor
Computer Security Department, New York State University at Farmingdale, New York, NY 11735, USA
Interests: cybersecurity; intrusion detection; machine learning

Special Issue Information

Dear Colleagues,

With the increasing complexity and diversity of network threats, Intrusion Detection Systems (IDSs) are confronted with more stringent performance demands, and the integration of artificial intelligence (AI) has become a key driver for their advancement.

This Special Issue focuses on emerging research trends and technologies in Intrusion Detection Systems (IDSs) and artificial intelligence (AI) and is intended to improve IDS designs. Interested researchers are invited to submit manuscripts that relate but are not limited to the following topics: artificial intelligence, optimization, quantum and post-quantum technologies, automation, software development, authentication, cryptography, and automated system designs for IDS improvements. This Special Issue particularly welcomes recent studies exploring traditional and quantum artificial intelligence and their applications in IDSs. All researchers, graduate students, and faculty of universities and other organizations are invited to participate in this publication by submitting novel research results that contribute theoretical and practical insights.

Focus areas include (but are not limited to) the following:

  • Intrusion detection systems;
  • Network security;
  • Application security;
  • Cloud security;
  • Endpoint security;
  • Data security;
  • Identity and access management;
  • Operational security;
  • Incidence response;
  • Forensics;
  • IoT security;
  • Mobile security;
  • Critical infrastructure security;
  • Cyber threat intelligence security;
  • Cryptography;
  • Governance, risk, and compliance;
  • Zero trust;
  • Physical security;
  • Human security;
  • Vulnerability management;
  • Machine learning;
  • Deep learning.

Dr. Emre Tokgoz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intrusion detection system
  • artificial intelligence
  • machine learning
  • deep learning
  • optimization
  • quantum and post-quantum technologies
  • automation
  • software solutions
  • authentication
  • cryptography
  • network system design
  • automated cryptographic system design

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Published Papers (15 papers)

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Research

32 pages, 3012 KB  
Article
MaGOS-IDS: A Mahalanobis-Enhanced OpenMax Method for Graph Neural Network-Based Intrusion Detection
by Thanh-Tung Nguyen and Minho Park
Electronics 2026, 15(16), 3667; https://doi.org/10.3390/electronics15163667 - 17 Aug 2026
Viewed by 176
Abstract
Graph Neural Networks achieve strong closed-set accuracy in network intrusion detection but cannot flag zero-day attacks, because the closed-world assumption forces every input into a known class. OpenMax adds an Extreme Value Theory reject option, yet its Euclidean distance ignores the class-conditional covariance [...] Read more.
Graph Neural Networks achieve strong closed-set accuracy in network intrusion detection but cannot flag zero-day attacks, because the closed-world assumption forces every input into a known class. OpenMax adds an Extreme Value Theory reject option, yet its Euclidean distance ignores the class-conditional covariance that encodes attack-specific structure, which produces unreliable tail models and rejection thresholds. We propose MaGOS-IDS, which extracts topology-aware embeddings with an edge-aware GCN that fuses flow-level edge features directly into message passing, whitens each class with a regularized Mahalanobis distance so the reject decision respects per-class variance and correlation, and calibrates a per-class EVT tail on these distances to set an attack-pattern-aware rejection boundary without a hand-tuned cutoff. We provide a theoretical justification via a peaks-over-threshold argument: whitening removes the per-class covariance dependence of the distance tail, so a single extreme-value tail model calibrates consistently across classes. On three benchmarks (NF-BoT-IoT, CIC-IDS-2017, UNSW-NB15) under withheld zero-day families, MaGOS-IDS raises open-set AU-PR over the Euclidean OpenMax baseline (0.932 vs. 0.848 on UNSW-NB15) while adding negligible inference cost. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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36 pages, 2585 KB  
Article
Quantum-Assisted Cross-Layer Intrusion Detection and Distributed-AI-Driven (QSec-DAI) Active Attribution of Insider and Man-in-the-Middle Attacks in Cooperative Sensing
by Iacovos Ioannou and Michael Georgiades
Electronics 2026, 15(16), 3620; https://doi.org/10.3390/electronics15163620 - 14 Aug 2026
Viewed by 138
Abstract
Cooperative sensing systems that exchange state estimates are exposed to two types of adversaries, namely, insiders who transmit correctly authenticated falsified content and outsiders who modify messages in transit after compromising a symmetric link key, each requiring a distinct mitigation strategy. These attacks [...] Read more.
Cooperative sensing systems that exchange state estimates are exposed to two types of adversaries, namely, insiders who transmit correctly authenticated falsified content and outsiders who modify messages in transit after compromising a symmetric link key, each requiring a distinct mitigation strategy. These attacks are observationally identical to a detector that examines only message content, although an insider must be revoked and an outsider must be countered through key rotation and link hardening. To distinguish between insider falsification and outsider in-transit message modification, a cross-layer intrusion detection and attack-attribution framework named QSec-DAI is proposed. Per-message anomaly scores are supplied by a recurrent detector, and a hybrid-symmetric, post-quantum and quantum authentication stack is arbitrated by belief-desire-intention agents under a finite-key budget. Authentication is used as an active probe because a suspicious link is hardened, and the persistence or disappearance of the anomaly is then observed. On real cooperative-localization data, an area under the receiver operating characteristic curve of 0.981 is achieved. Benign, insider and outsider classes are attributed with a macro-averaged accuracy of 0.794 and a man-in-the-middle recall of 0.719. Under the explicitly defined attribution mapping, outsider recall is zero for the evaluated baselines that remain in fixed-symmetric mode after key exposure. In the real-data evaluation, malicious influence on fusion is limited to 0.10 percent. The no-cooperation control indicates that several classical defenses suppress attacks mainly by discarding cooperative information rather than by preserving useful cooperation. Protocol-level fault injection shows that replay is rejected while monotonic freshness state is intact, whereas compromise of the verifier or of all independent strong credentials removes defensible outsider identifiability. The quantum component is therefore presented as one resource-constrained strong-authentication option rather than as a source of quantum-enhanced anomaly detection. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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24 pages, 1129 KB  
Article
A Privacy-Preserving Middleware Architecture for Detecting Prompt Injection and Sensitive Data Exposure in Large-Language-Model Interactions
by Adam Ait Hsine and Abdullahi Arabo
Electronics 2026, 15(16), 3554; https://doi.org/10.3390/electronics15163554 - 11 Aug 2026
Viewed by 175
Abstract
The deployment of large language models (LLMs) in real-world applications introduces a compounding security problem: detecting adversarial inputs such as prompt injection and jailbreak-driven data leakage while simultaneously preventing the detection mechanism itself from becoming a source of data exposure. Existing approaches address [...] Read more.
The deployment of large language models (LLMs) in real-world applications introduces a compounding security problem: detecting adversarial inputs such as prompt injection and jailbreak-driven data leakage while simultaneously preventing the detection mechanism itself from becoming a source of data exposure. Existing approaches address either detection effectiveness or privacy preservation, but rarely both in a unified, deployable architecture. This paper proposes and evaluates a privacy-preserving hybrid middleware architecture that enforces a local trust boundary as its primary design constraint. The architecture combines deterministic rule-based screening, a fine-tuned small language model (SLM) operating entirely within the local processing environment, and a sensitivity-aware routing mechanism that invokes external LLM reasoning only for prompts all local components have assessed as non-sensitive. Evaluation on a 120-prompt benchmark spanning benign, jailbreak, and sensitive categories (including 20 hard negatives constructed to be lexically adjacent to genuine secrets) shows that the routed architecture attains 95.83% accuracy with complete recall, retaining 95% of sensitive prompts within the local boundary, at the cost of a 12.5% false-positive rate. Comparison against two published detectors reveals a systematic asymmetry: an injection-specific classifier reaches 82.5% recall on jailbreak prompts but 25% on sensitive ones, while a content-safety model inverts that profile, confirming empirically that the two risks are addressed separately by current tooling. The framework is model-agnostic, requires no retraining of the underlying LLM, and is compatible with black-box API deployments. The evaluation dataset and fine-tuned model are released publicly. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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39 pages, 1370 KB  
Article
TRACE: Feature-Space Feasible Action Contracts for Explainable Intrusion Triage
by Tran Duc Le, Mohammad Arifuzzaman and Yida Bao
Electronics 2026, 15(15), 3291; https://doi.org/10.3390/electronics15153291 - 26 Jul 2026
Viewed by 272
Abstract
Explainable intrusion detection systems often provide feature attributions without indicating whether a security action should be released, downgraded, or deferred. This paper investigates whether action-governed explanations can provide bounded triage evidence under traffic feature feasibility constraints. We present TRACE, a framework that maps [...] Read more.
Explainable intrusion detection systems often provide feature attributions without indicating whether a security action should be released, downgraded, or deferred. This paper investigates whether action-governed explanations can provide bounded triage evidence under traffic feature feasibility constraints. We present TRACE, a framework that maps calibrated detector outputs to a finite action ladder, constructs conformal action sets, selects actions via a utility-minimax rule, and releases high-severity actions only when compact support contracts remain stable under feasible perturbations, where feasibility is a property of the processed benchmark features and not of packet-level realizability. Ablations isolate the conformal set and release gate as the primary drivers of system behavior. Across 11 gated dataset–model pairs, TRACE produces non-degenerate action sets with zero full-set collapse and defer/block rates from 0.603 to 1.000. Under held-out sample split tuning, it achieves higher average proxy utility than unconditional release and release rate-matched random release on all 11 pairs. Against the strongest simple selective gate, however, it matches on 6 of 11 pairs and trails on the remaining 5. Robustness sweeps confirm positive all-row utility on all pairs, though pass-only utility becomes fragile in ultra-low-release regimes. Unlike display-only attribution summaries, the TRACE contract records the plausible action set, feasibility checks, stability summaries, and an explicit release rationale. The results support TRACE as a bounded evidentiary framework for action-governed XAI in IDS, rather than claiming superiority over all IDS/XAI methods or general deployment readiness. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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32 pages, 4685 KB  
Article
Cost-Sensitive Stacking Ensemble with Hybrid Feature Selection for Rare Attack Detection in Network Intrusion Detection Systems
by Ioan Corneliu Salisteanu, Iulian Udroiu, Andrei Cosmin Gheorghe, Ionut Adrian Tudoroiu and Emil Mihai Diaconu
Electronics 2026, 15(14), 3094; https://doi.org/10.3390/electronics15143094 - 14 Jul 2026
Viewed by 319
Abstract
Machine learning-based network intrusion detection systems are often optimized using aggregate accuracy, although operational security depends on the reliable detection of rare, high-impact attacks. This paper proposes a data-preserving intrusion detection framework that combines hybrid feature selection, heterogeneous ensemble learning and cost-sensitive optimization [...] Read more.
Machine learning-based network intrusion detection systems are often optimized using aggregate accuracy, although operational security depends on the reliable detection of rare, high-impact attacks. This paper proposes a data-preserving intrusion detection framework that combines hybrid feature selection, heterogeneous ensemble learning and cost-sensitive optimization for imbalanced multi-class attack detection. The method first applies Mutual Information filtering and Recursive Feature Elimination to reduce the NSL-KDD feature space from 122 one-hot encoded attributes to 25 discriminative features. Four classifiers, Random Forest, XGBoost, Support Vector Machine and K-Nearest Neighbors, are evaluated individually, and a stacking ensemble is constructed using Logistic Regression as a meta-learner. Class imbalance is addressed by balanced class weighting rather than by synthetic oversampling, preserving the original minority-class observations. Experiments on the NSL-KDD benchmark show that the proposed cost-sensitive configuration improves rare attack recognition, most notably increasing U2R recall from 0.00% to 35.82% (24 of 67 test instances) for the stacking ensemble; this improvement, together with the accompanying weighted F1-score change from 0.7120 to 0.7214, is statistically significant under the Wilcoxon signed-rank test across repeated random seeds, and both values are reported with their variability rather than as single point estimates. SVM obtains the largest global gain, with a 7.06 percentage point improvement in weighted F1-score. The results show that cost-sensitive learning is a simple and practical mechanism for improving rare-attack visibility, but also reveal a remaining limitation for R2L detection, where feature overlap with Normal traffic remains substantial. The revised validation design explicitly includes direct resampling baselines, repeated-seed evaluation, statistical significance testing, feature-subset sensitivity analysis, and absolute true-positive counts for R2L and U2R in order to avoid overinterpreting marginal point-estimate gains. All experiments, including the resampling comparison, the component ablation, the feature-subset sensitivity analysis and the repeated-seed statistical evaluation, are executed on the complete KDDTrain+ training set of 125,973 instances under a single unified protocol, so that every reported per-class value refers to the same experimental setting. The revised study additionally reports probability-level evaluation for the primary model, including class-level PR-AUC, precision-recall curves and a U2R threshold and alert-budget analysis, and validates the framework externally on the UNSW-NB15 benchmark, where balanced class weighting raises the recall of the rarest categories (Worms, Shellcode, Backdoor) from near-zero baseline levels to 69–96% under an identical protocol. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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37 pages, 1346 KB  
Article
Scenario-Aware Federated Intrusion Detection for V2X-Inspired Edge Security: Calibration, Heterogeneity, and Communication Analysis
by Manuel J. Cabral S. Reis
Electronics 2026, 15(14), 3090; https://doi.org/10.3390/electronics15143090 - 14 Jul 2026
Viewed by 273
Abstract
Vehicle-to-Everything (V2X) communication systems are becoming a foundational component of intelligent transportation systems, but their increasing connectivity also enlarges the cyberattack surface and raises important privacy and deployment challenges for intrusion detection. Conventional centralized intrusion detection systems can achieve strong predictive performance, yet [...] Read more.
Vehicle-to-Everything (V2X) communication systems are becoming a foundational component of intelligent transportation systems, but their increasing connectivity also enlarges the cyberattack surface and raises important privacy and deployment challenges for intrusion detection. Conventional centralized intrusion detection systems can achieve strong predictive performance, yet they require aggregation of sensitive traffic data and may be difficult to deploy across distributed edge environments. This study presents a federated learning-based intrusion detection evaluation framework for privacy-aware and deployment-oriented security monitoring in V2X-inspired distributed environments. Rather than proposing a new detection architecture, the work focuses on a more rigorous and realistic assessment protocol for federated intrusion detection under scenario shift, client heterogeneity, threshold-sensitive operation, and communication constraints. The proposed approach combines group-based train/test partitioning to better reflect scenario separation, lightweight multilayer perceptron (MLP) models suitable for edge-side training, and explicit analysis of communication overhead and threshold calibration. Using the CICIDS2017 dataset as a controlled proxy benchmark, the study compares centralized baselines, local-only learning, FedAvg, and FedProx for binary intrusion detection under approximately IID and strongly non-IID client partitions. The experimental protocol uses equal training-set sizes across centralized and federated methods, an independent calibration set for threshold and checkpoint selection, and five independent random seeds, with results reported as mean ± standard deviation. The results show that the stricter group-based evaluation protocol substantially reduces performance compared with optimistic random-split evaluation, confirming the importance of scenario-aware validation for intrusion detection. Under the protocol, the best mean F1-score was obtained by FedAvg in the strong non-IID configuration, with an F1-score of 0.468 ± 0.028, followed closely by FedAvg under approximately IID partitioning with 0.463 ± 0.036. Centralized MLP, logistic regression, and random forest baselines achieved comparable but slightly lower F1-scores, indicating that federated learning remained competitive rather than clearly superior under this challenging setting. The analysis further shows that threshold calibration on an independent calibration set materially changes the operating point of the detectors, while validation-selected federated checkpoints generally occurred in later communication rounds within the tested 15-round budget. Communication analysis showed that the lightweight MLP required only approximately 72 kB per model update, corresponding to about 720 kB per federated round when both uplink and downlink traffic were counted for five clients. Overall, the findings support federated learning as a viable and communication-efficient direction for privacy-aware intrusion detection in distributed edge-security settings, while also highlighting the need for cautious interpretation, native V2X validation, and future robustness analysis against compromised federated clients. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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41 pages, 2880 KB  
Article
A Comparative Study of Large Language Models for Industrial Cyber-Physical Security
by J. de Curtò, I. de Zarzà, Juan Carlos Cano and Carlos T. Calafate
Electronics 2026, 15(13), 2779; https://doi.org/10.3390/electronics15132779 - 24 Jun 2026
Viewed by 419
Abstract
Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution [...] Read more.
Intrusion detection in industrial cyber-physical systems is constrained by small labelled-attack corpora and by the subtler signal of physical-process attacks compared with classical IT-network intrusions, motivating renewed interest in foundation-model-based detectors; classical detectors are typically trained per dataset and degrade under the distribution shift that is common in operational technology, where attack repertoires evolve faster than retraining cycles. Two foundation-model families are now plausible candidates: open-source Large Language Models (LLMs) and recent tabular foundation models (TabPFN, TabICL) pre-trained for in-context tabular inference. We compare the two families head-to-head, alongside Random Forest and XGBoost classical anchors, across three established industrial security benchmarks (SWaT, HAI, WUSTL-IIoT-2021) under a controlled multi-seed full-holdout protocol with paired McNemar and cross-seed Mann–Whitney tests. The empirical picture is dataset-dependent rather than universal: tabular foundation models establish a strong, previously unreported baseline that is competitive with or superior to classical anchors on every dataset evaluated, while LLMs are complementary detectors with a specific advantage on schemas that carry process-engineering semantics (such as SWaT’s named sensor channels). A per-class analysis on the WUSTL five-class attack taxonomy shows that the two families have structurally different strengths: tabular methods dominate traffic-rich attacks (Denial-of-Service, Reconnaissance), whereas LLMs are competitive on rare attack types (Backdoor, Command Injection). A confidence-gated cascade that escalates only low-confidence tabular decisions to an LLM exceeds either detector alone at a small query budget, and a leave-one-attack-type-out analysis shows that foundation-model detectors generalise to unseen attack families substantially better than the classical anchors. The appropriate detector choice in industrial cyber-physical security is therefore informed by the dataset’s feature schema, the attack-type mix, and the operational cost envelope, rather than by a specific performance metric. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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25 pages, 4347 KB  
Article
A Technology-Centric Cyber Resilience Evaluation Framework Using MITRE D3FEND for Bridging the Policy Technology Gap in Financial and Enterprise Environments
by GwangHyun Ahn and Dongkyoo Shin
Electronics 2026, 15(12), 2554; https://doi.org/10.3390/electronics15122554 - 9 Jun 2026
Viewed by 331
Abstract
Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study [...] Read more.
Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study proposes D3-CREF, a technology-centric cyber resilience evaluation framework that maps the MITRE D3FEND taxonomy to financial security domains and introduces a Normalized Resilience Index (NRI) aggregating four dimensions—Coverage, Maturity, Automation, and Timeliness—via a closed-form weighted geometric mean with AHP-elicited weights (consistency ratio CR = 0.04). All NRI indicators are anchored to MITRE ATT&CK techniques and exemplar CVE entries, enabling threat-informed measurement. The framework was validated through a three-round Delphi study with 50 experts (Kendall’s W = 0.78, p < 0.001; Cronbach’s α = 0.89; CVR 0.68–0.92) and a Cyber Range-based simulation. For three institutions with identical BoK scores (92/100), NRI yielded discriminative values of 0.83, 0.44, and 0.09 (CV = 0.68 vs. 0.00 for the baseline), confirming a shift from compliance-based to performance-driven assessment. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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14 pages, 734 KB  
Article
An Agent-Based Model of a Controlled Detonation System for Sandbox Analysis of Suspicious Software
by Yevheniia Ivanchenko, Mikolaj Karpinski, Mykola Ryzhakov, Ihor Ivanchenko, Patryk Mazurek and Pawel Sawicki
Electronics 2026, 15(11), 2348; https://doi.org/10.3390/electronics15112348 - 28 May 2026
Viewed by 417
Abstract
In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to [...] Read more.
In this paper, we present an agent-based model of a controlled detonation system for dynamic sandbox analysis of suspicious software. Instead of treating the sandbox as a passive observer, the model places an AI operator inside the analysis loop and allows it to perform adaptive GUI interactions in a plausible, isolated execution environment. The controlled detonation process is formulated as a partially observable Markov decision process (POMDP), while the proposed proof-of-concept architecture combines initial profiling, VM preparation, multi-layer telemetry, and an RL policy with visual perception and temporal memory. Evaluation in a controlled emulation setting on 180 malware samples from three threat classes shows higher Activity Rates and Coverage, and shorter Time-to-Reveal than passive and fixed scripted baselines. These results support the feasibility of adaptive interactions as a promising direction for sandbox analysis, while broader external validation, matched comparisons with prior systems, and component-wise ablation remain future work. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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30 pages, 7038 KB  
Article
Distributional Drift in IoT Intrusion Detection Systems: Implications for Cross-Dataset Generalisation
by Kazım Kıvanç Eren, Kerem Küçük, Radhwan A. A. Saleh, Mehmet Zeki Konyar, Olympia M. Hardy and Sajjad Ahmad Khan
Electronics 2026, 15(11), 2307; https://doi.org/10.3390/electronics15112307 - 26 May 2026
Viewed by 617
Abstract
The rapid expansion of Internet of Things (IoT) technologies has highlighted the need for reliable intrusion detection systems (IDSs), yet the majority of existing studies rely on single-dataset evaluations, raising concerns about their real-world generalisation capability. This study addresses this limitation by systematically [...] Read more.
The rapid expansion of Internet of Things (IoT) technologies has highlighted the need for reliable intrusion detection systems (IDSs), yet the majority of existing studies rely on single-dataset evaluations, raising concerns about their real-world generalisation capability. This study addresses this limitation by systematically investigating distributional shift across heterogeneous IoT intrusion detection datasets and their impact on model behaviour. To achieve this, a unified feature space is constructed using BoT-IoT, ToN-IoT, and UNSW-NB15 datasets, followed by a comprehensive preprocessing pipeline including attack class alignment, distribution-preserving sampling for class imbalance, and feature selection based on cross-dataset feature value propagation analysis. Furthermore, feature-specific transformations and correlation-based dimensionality reduction are applied to enhance statistical consistency and model stability. To simulate realistic deployment scenarios, models are trained on combinations of datasets and evaluated on unseen datasets. The results reveal that distributional inconsistencies and dataset-specific feature biases significantly degrade cross-dataset performance, despite strong within-dataset results. The proposed framework provides a systematic understanding of feature-level behaviour across datasets, identifying both stable and bias-prone features. These findings highlight the necessity of distribution-aware preprocessing and feature analysis for developing robust and generalisable IoT intrusion detection systems. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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31 pages, 917 KB  
Article
X-GATE: Attribution-Aware Distillation and Hardening for Compressed Edge-IIoT Intrusion Detection
by Tran Duc Le, Yida Bao and Mohammad Arifuzzaman
Electronics 2026, 15(11), 2284; https://doi.org/10.3390/electronics15112284 - 25 May 2026
Viewed by 416
Abstract
Industrial Internet of Things (IIoT) intrusion detection requires compact, latency-efficient models whose behavior remains assessable under adversarial stress, yet compression can alter the feature-attribution structure learned by a full-precision model. This paper presents X-GATE (eXplanation-Guided Adversarial Training Engine), an attribution-aware training framework for [...] Read more.
Industrial Internet of Things (IIoT) intrusion detection requires compact, latency-efficient models whose behavior remains assessable under adversarial stress, yet compression can alter the feature-attribution structure learned by a full-precision model. This paper presents X-GATE (eXplanation-Guided Adversarial Training Engine), an attribution-aware training framework for compressed Edge-IIoT intrusion detection. X-GATE combines Explanation-Consistency Distillation (ECD), which aligns Teacher–Student feature-attribution rankings with a differentiable soft-rank Spearman penalty, and Explanation-Guided Adversarial Training (EGAT), which hardens the Student on Teacher-salient feature coordinates. On the full Edge-IIoTset 2022 benchmark, the latest three-seed ablation gives Full X-GATE 89.30 ± 3.89% F1-Macro with 0.617 M parameters, within approximately 0.6 percentage points of the full-precision Teacher; a Random Forest model remains a stronger clean-F1 reference, so X-GATE is not framed as the clean-accuracy optimum. In a separate deployment-subset rerun, X-GATE obtains 78.83 ± 5.83% float F1-Macro and 79.11 ± 5.47% INT8 F1-Macro, reduces the adversarial false-positive rate from 0.46 ± 0.08% for KD-only to 0.16 ± 0.09% under the evaluated single-step white-box explanation-evasion protocol, and reduces CPU latency from 4.16 to 1.25 ms/sample. Component ablation further shows that ECD reduces Logical Drift by 17.24%, while EGAT improves adversarial F1 by 10.57 percentage points. Taken together, these benchmark- and protocol-bounded results position X-GATE as a compact neural operating point for the Edge-IIoT setting studied here, balancing attribution consistency, targeted hardening, and CPU-side efficiency. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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26 pages, 12505 KB  
Article
Hardware–Software Co-Optimized Lightweight Real-Time CAN Intrusion Detection and Prevention System for ECUs
by Youngmin Jang, Hyungchul Im, Jonggwon Kim, Semin Kim, Eunsu Kim and Seongsoo Lee
Electronics 2026, 15(10), 2108; https://doi.org/10.3390/electronics15102108 - 14 May 2026
Viewed by 557
Abstract
The Controller Area Network (CAN) protocol used in in-vehicle networks is vulnerable to external attacks because it lacks authentication and encryption mechanisms. Accordingly, CAN Intrusion Detection Systems (IDSs) have been studied. However, existing IDSs remain difficult to deploy in practical vehicles because of [...] Read more.
The Controller Area Network (CAN) protocol used in in-vehicle networks is vulnerable to external attacks because it lacks authentication and encryption mechanisms. Accordingly, CAN Intrusion Detection Systems (IDSs) have been studied. However, existing IDSs remain difficult to deploy in practical vehicles because of their limited real-time capability, complex preprocessing, and high computational cost. To overcome these limitations, this paper proposes an ultra-lightweight Convolutional Neural Network (CNN)-based IDS that significantly reduces parameters and computational complexity while maintaining high detection performance. The proposed IDS improves area efficiency through a streaming pipeline, computation-block reuse, and constrained Processing Element (PE) parallelism. In addition, its lightweighting effect was quantitatively evaluated against an RTL baseline implemented under identical platform and design constraints. When an attack is detected, an Intrusion Prevention System (IPS) integrated with the CAN controller generates an error frame to block it in real time. The proposed IDS achieved over 99.97% detection performance for known frame-level message-injection scenarios on the Car-Hacking Dataset. It also achieved branch-wise real-time feasibility with an 11.46 µs ID-branch precomputation latency and a 5.68 µs DATA-complete-to-decision latency at 50 MHz. In TSMC 28 nm ASIC synthesis, the proposed IDS required 70,592 gates, with an estimated ASIC power of 2.0231 mW and an active inference energy of 34.68 nJ. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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16 pages, 919 KB  
Article
A Comparative Performance Study of Host-Based Intrusion Detection Using TextRank-Based System Call Preprocessing and Deep Learning Models
by Hyunwook You, Chulgyun Park, Dongkyoo Shin and Dongil Shin
Electronics 2026, 15(9), 1856; https://doi.org/10.3390/electronics15091856 - 27 Apr 2026
Viewed by 622
Abstract
Host-based intrusion detection systems (HIDSs) can address the limitations of network-based detection by analyzing system calls and other low-level events. Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set [...] Read more.
Host-based intrusion detection systems (HIDSs) can address the limitations of network-based detection by analyzing system calls and other low-level events. Many existing benchmark datasets remain inadequate for evaluating modern attacks because they were built in outdated environments and cover only a limited set of attack behaviors. To address this gap, this study builds a TextRank-based preprocessing pipeline on the LID-DS 2021 dataset and compares five end-to-end pipelines: Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network(CNN) + LSTM, LSTM, Bidirectional LSTM (BiLSTM), and CNN + Bidirectional Gated Recurrent Unit (BiGRU). Of the 15 scenarios in the dataset, six multi-stage attacks were excluded, and three representative scenarios were selected based on attack-category coverage and suitability for single-chunk host-level detection. Within these three selected scenarios and same-scenario file-level splits, the deep learning pipelines achieved F1-scores of 0.90–0.94, whereas RF ranged from 0.55 to 0.63. Among the evaluated pipelines, CNN + BiGRU produced the strongest overall results. These findings indicate that, under this constrained evaluation setting, sequential deep learning pipelines can be effective for scenario-specific system-call-based HIDS; however, broader generalization to unseen attacks or to the full LID-DS 2021 scenario set remains unverified. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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27 pages, 3484 KB  
Article
Enhancing RMF and ATT&CK Mapping Accuracy Through Integration of Sentence-BERT and Mitigation Parameters
by Hanhee Lee, Sukjoon Yoon, Yunkyung Lee and Jiwon Kang
Electronics 2026, 15(6), 1248; https://doi.org/10.3390/electronics15061248 - 17 Mar 2026
Viewed by 785
Abstract
To minimize cybersecurity risks in weapon systems, the implementation of the Korean Risk Management Framework (K-RMF) has become imperative. However, a significant “strategic gap” exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated [...] Read more.
To minimize cybersecurity risks in weapon systems, the implementation of the Korean Risk Management Framework (K-RMF) has become imperative. However, a significant “strategic gap” exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated mitigation-driven pipeline that integrates Sentence-BERT (SBERT) with the structural defense relationships of the ATT&CK knowledge graph. To address the data coverage limitations of the Center for Threat-Informed Defense (CTID) silver standard, we introduce Recall@restricted as a calibrated performance metric. Experimental evaluations demonstrate that the proposed ensemble framework achieves a Recall@restricted of 0.74, significantly outperforming baseline SBERT-only models. These findings suggest that deterministic mitigation relationships effectively complement semantic representations, providing a robust framework for aligning RMF controls with adversarial behaviors. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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24 pages, 3150 KB  
Article
An Intrusion Detection Model Based on Equalization Loss and Spatio-Temporal Feature Extraction
by Miaolei Deng, Shaojun Fan, Yupei Kan and Chuanchuan Sun
Electronics 2026, 15(3), 646; https://doi.org/10.3390/electronics15030646 - 2 Feb 2026
Cited by 1 | Viewed by 773
Abstract
In recent years, the expansion of network scale and the diversification of attack methods pose dual challenges to intrusion detection systems in extracting effective features and addressing class imbalance. To address these issues, the Spatial–Temporal Equilibrium Graph Convolutional Network (STEGCN) is proposed. This [...] Read more.
In recent years, the expansion of network scale and the diversification of attack methods pose dual challenges to intrusion detection systems in extracting effective features and addressing class imbalance. To address these issues, the Spatial–Temporal Equilibrium Graph Convolutional Network (STEGCN) is proposed. This model integrates Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU), leveraging GCN to extract high-order spatial features from network traffic data while capturing complex topological relationships and latent patterns. Meanwhile, GRU efficiently models the dynamic evolution of network traffic over time, accurately depicting temporal trends and anomaly patterns. The synergy of these two components provides a comprehensive representation of network behavior. To mitigate class imbalance in intrusion detection, the Equalization Loss v2 (EQLv2) is introduced. By dynamically adjusting gradient contributions, this function reduces the dominance of majority classes, thereby enhancing the model’s sensitivity to minority-class attacks. Experimental results demonstrate that STEGCN achieves superior detection performance on the UNSW-NB15 and CICIDS2017 datasets. Compared with traditional deep learning models, STEGCN shows significant improvements in accuracy and recall, particularly in detecting minority-class intrusions. Full article
(This article belongs to the Special Issue Emerging Research Trends and Technologies in Intrusion Detection Systems (IDSs) and Artificial Intelligence (AI) Utilization)
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