Caiyun Li^1,#, Xiaowen Liu^1,#, Binhui Tang^1,*, Tingting Lu², Daibo Xiao³, Li Chen³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086522 - 13 August 2026

Abstract Unmanned aerial vehicle (UAV) communication links in low-altitude operations support mission control, status feedback, and data transmission. Abnormal communication states may affect mission continuity and risk response. However, conventional anomaly detection usually determines only whether a communication anomaly exists, making it difficult to support risk-state assessment and alert-threshold decision-making. To address this problem, this paper proposes Risk Stratification and Task-cost-aware Decision via Knowledge Distillation (RSTD-KD), a task-cost-aware UAV communication risk warning approach that integrates risk stratification, probability calibration, and threshold decision-making. RSTD-KD aggregates fine-grained communication records into window-level behavior samples, constructs low-, medium-, and high-risk… More >

Mian Muhammad Kamal^1,*, Tianjun Ma^1,*, Mohammed K. Alzaylaee², Husam S. Samkari^3,4, Mohammed F. Allehyani³, Omar Almomani⁵, Heba G. Mohamed^6,7

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085412 - 13 August 2026

Abstract The cyber-physical network (CPS) combines sensing, communication, and control in physical processes, making them very susceptible to sophisticated cyber-attacks that may cause safety-critical effects. There are two core shortcomings to existing intrusion detection systems (IDS): generative-only models have little transparency of decision-making, while explainable-only models have low robustness in the presence of imbalanced and zero-day attacks. This paper presents a sequentially integrated trustworthy intrusion detection (ID) framework that combines generative learning and explainable AI (XAI) to boost robustness and transparency. The generative module enhances training data diversity, while the explainability module provides post-hoc interpretations during… More >

Ye Lu¹, Haoyang Hu^1,*, Wenyi Chang¹, Wanbin Liu¹, Wenyuan Zhang²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084490 - 13 August 2026

Abstract Fine-grained multiclass intrusion detection over flow-level traffic remains difficult, largely because class boundaries are often entangled, temporal dependence is non-negligible, and the label distribution is heavily long-tailed. In this study, a compact temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-TinyTransformer framework is developed to bring these issues into a single modeling pipeline: the TCN branch focuses on short-range anomalous patterns, the BiGRU branch captures bidirectional temporal structure, and the TinyTransformer branch complements them with broader contextual interaction learning. To reduce the bias induced by extreme imbalance, training is not driven by a single correction mechanism, More >

Muhammad Mujahid¹, Fatima Alshannaq¹, Shaha Al-Otaibi², Tanzila Saba^1,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084273 - 13 August 2026

Abstract Cybersecurity threats in Internet of Things (IoT) networks have escalated, enabled by rapid advancements in wireless communication and edge computing technologies. These advancements expose networks to a wide range of sophisticated and evolving threats and increasingly complex research challenges. Traditional Intrusion Detection and Prevention Systems (IDS/IPS) often fail to provide reliable performance regarding the flexibility and scalability required to handle evolving attack patterns. This study proposes an APENet approach to detect cyberattacks from a real-world cybersecurity dataset, and incorporated a contextual dependency mechanism. The approach captures both local transition dependencies and global relational interactions within… More >

Sultan Shutyan Albalawi¹, Mohd Yamani Idna Idris^1,2,*, Ainuddin Wahid Bin Abdul Wahab¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083860 - 13 August 2026

Abstract As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the More >

Alanoud Al Mazroa¹, Abdulrahman Mohammed Alamoudi², Nurdaulet Karabayev³, Jawad Ahmad^4,*, Muhammad Shahbaz Khan⁵

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083381 - 13 August 2026

Abstract The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these… More >

Canan Batur Şahin^1,*, Siti Fatimah Abdul Razak^2,*, Arif Ullah², Ali Fatih Gündüz¹, Nazri Mohd Nawi³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.080874 - 13 August 2026

Abstract Unmanned Aerial Vehicle (UAV) networks face escalating cybersecurity threats, especially from zero-day attacks that exploit previously unknown vulnerabilities. To address this, we present HADAR-UAV (Hybrid Anomaly Detection with Adaptive Risk-calibration for UAV). This novel intrusion detection framework integrates masked autoencoder representation learning with Deep Support Vector Data Description (Deep SVDD) under conformal prediction guarantees to calibrate risk. Our method overcomes three critical limitations of existing approaches: (i) over-reliance on attack signatures, (ii) lack of statistical guarantees on false alarm rates, and (iii) insufficient robustness in feature extraction under partial observation. Using a rigorous Leave-Two-Attack-Families-Out (L2AFO)… More >

Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Chao-Tung Yang^3,4,5, Wei-Zong Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083880 - 27 July 2026

Abstract Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,… More >

Yeog Kim, Changhoon Lee, Kiwook Sohn^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083669 - 27 July 2026

Abstract With the widespread adoption of encryption protocols, payload-based traffic analysis has become increasingly infeasible, posing significant challenges for intrusion detection systems (IDS). Consequently, AI-based approaches for encrypted traffic analysis have gained substantial attention. However, existing studies are often evaluated using inconsistent criteria, including heterogeneous attack labels, behavioral representations, and model architectures, making systematic comparison difficult. To address this limitation, this paper proposes a three-level analytical taxonomy for encrypted traffic analysis, structured around attack objectives (Level 1), observable network behaviors (Level 2), and detection models (Level 3). The proposed framework provides a structured perspective for analyzing… More >

Md. Fahmid-Ul-Alam Juboraj¹, Fahmid Al Farid^2,3, Mahe Zabin⁴, Jia Uddin⁵, Muhammad Iqbal Hossain^1,*, Sarina Mansor^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081254 - 27 July 2026

Abstract The integration of Internet of Things (IoT) technologies in agriculture enables precision farming but introduces significant cybersecurity vulnerabilities. This paper presents FICNet (Feature Integrated Convolutional Network), a lightweight deep learning architecture for intrusion detection in agricultural IoT environments. Evaluated on the Farm-Flow AG-IoT security dataset, FICNet achieves 100% binary classification accuracy and 81.25% multiclass accuracy (macro F1: 80.43%, precision: 91.26%, ROC-AUC: 96.78%) across 8 traffic categories. A multi-dimensional component analysis confirms the contribution of each architectural component: multi-scale convolutions provide 5.3% noise robustness advantage, squeeze-and-excitation attention controls per-class detection trade-offs, and the full architecture achieves More >