Penerapan Kecerdasan Buatan dalam Keamanan Siber pada Infrastruktur Kritis: Tinjauan Sistematis terhadap Ancaman, Solusi, dan Tantangan
DOI:
https://doi.org/10.58776/jriti.v3i2.232Kata Kunci:
Keamanan Siber, Infrastruktur Kritis, Kecerdasan Buatan, Sistem Kontrol Insdustri, Tinjauan Pustaka SistematisAbstrak
Infrastruktur kritis seperti sistem energi, jaringan distribusi air, fasilitas kesehatan, dan sistem transportasi merupakan tulang punggung operasional masyarakat modern yang sangat bergantung pada teknologi digital dan sistem cyber-physical. Konvergensi teknologi informasi (IT) dan teknologi operasional (OT) dalam Industrial Control Systems (ICS) dan SCADA telah meningkatkan efisiensi operasional, namun secara bersamaan memperluas permukaan serangan yang dapat dieksploitasi oleh aktor jahat. Artikel ini menyajikan tinjauan sistematis terhadap 89 publikasi periode 2020-2025 untuk menganalisis perkembangan penerapan kecerdasan buatan (AI) dalam keamanan siber infrastruktur kritis. Hasil kajian mengidentifikasi enam kategori utama serangan siber, diantaranya serangan terhadap jaringan dan komunikasi industri, manipulasi data dan injeksi perintah, Advanced Persistent Threats (APT), malware dan ransomware, insider threats, serta ancaman terhadap sistem berbasis AI. Penelitian ini menunjukkan bahwa algoritma AI, termasuk deep learning (CNN, LSTM, Transformer), machine learning klasik (Random Forest, SVM), Generative Adversarial Networks (GAN), Reinforcement Learning, dan Federated Learning memberikan kontribusi signifikan dalam deteksi dini anomali, respons adaptif, dan pemulihan sistem dengan tingkat akurasi mencapai 96-99%. Namun, implementasi AI menghadapi tantangan berupa kompleksitas komputasi tinggi, keterbatasan dataset, kerentanan terhadap adversarial attacks, serta kebutuhan transparansi dan interpretabilitas. Artikel ini merekomendasikan pengembangan model hybrid yang efisien, integrasi explainable AI, penerapan federated learning lintas sektor, dan pembentukan kerangka kolaboratif untuk membangun sistem keamanan siber yang tangguh dan berkelanjutan.
Referensi
N. Neshenko, E. Bou-Harb, B. Furht, and M. Baghersad, “A deep learning-based adaptive cyber disaster management framework,” J Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01241-3.
B.-V. Vasilică, F.-D. Anton, R. Pietraru, S.-O. Anton, and B.-N. Chiriac, “Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems,” Applied Sciences (Switzerland), vol. 15, no. 9, 2025, doi: 10.3390/app15094596.
A. Dehlaghi-Ghadim, A. Balador, M. H. Moghadam, H. Hansson, and M. Conti, “ICSSIM — A framework for building industrial control systems security testbeds,” Comput Ind, vol. 148, 2023, doi: 10.1016/j.compind.2023.103906.
G. Amritha, M. G. Nair, and F. Granelli, “Machine Learning-Enriched Cybersecurity in Smart Grids,” IEEE Access, vol. 13, pp. 99303–99313, 2025, doi: 10.1109/ACCESS.2025.3576497.
N. Muller, K. Bao, J. Matthes, and K. Heussen, “CyPhERS: A cyber-physical event reasoning system providing real-time situational awareness for attack and fault response,” Comput Ind, vol. 151, 2023, doi: 10.1016/j.compind.2023.103982.
L. SAMBUCCI and E.-A. PARASCHIV, “The accelerated integration of artificial intelligence systems and its potential to expand the vulnerability of the critical infrastructure,” Revista Română de Informatică și Automatică, vol. 34, no. 3, pp. 113–148, 2024, doi: 10.33436/v34i3y202410.
M. Barbhaya, P. R. Dasari, S. K. Damarla, R. Srinivasan, and B. Huang, “A deep learning framework for cyberattack detection and classification in Industrial Control Systems,” Comput Chem Eng, vol. 202, 2025, doi: 10.1016/j.compchemeng.2025.109278.
V. Maurya, R. Agarwal, S. Kumar, and S. Shukla, “EPASAD: ellipsoid decision boundary based Process-Aware Stealthy Attack Detector,” Cybersecurity, vol. 6, no. 1, 2023, doi: 10.1186/s42400-023-00162-z.
S. Mokhtari and K. K. Yen, “False Data Injection Attack Detection, Isolation, and Identification in Industrial Control Systems Based on Machine Learning: Application in Load Frequency Control,” Electronics (Switzerland), vol. 13, no. 16, 2024, doi: 10.3390/electronics13163239.
S. Kabir, N. Hannan, A. Shufian, and M. S. Rahman Zishan, “Proactive detection of cyber-physical grid attacks: A pre-attack phase identification and analysis using anomaly-based machine learning models,” Array, vol. 27, 2025, doi: 10.1016/j.array.2025.100441.
A. M. Awaad, K. M. Ali Alheeti, and A. K. A. H. Najem, “Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems,” Mesopotamian Journal of Big Data, vol. 2024, pp. 230–240, 2024, doi: 10.58496/MJBD/2024/017.
A. Salehiyan, P. S. Moghaddam, and M. Kaveh, “An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets,” Future Internet, vol. 17, no. 7, 2025, doi: 10.3390/fi17070279.
R. Bhukya, S. A. Moeed, A. Medavaka, A. O. Khadidos, A. O. Khadidos, and S. Selvarajan, “SPARK and SAD: Leading-edge deep learning frameworks for robust and effective intrusion detection in SCADA systems,” International Journal of Critical Infrastructure Protection, vol. 49, 2025, doi: 10.1016/j.ijcip.2025.100759.
J. Wang, C. Si, Z. Wang, and Q. Fu, “A New Industrial Intrusion Detection Method Based on CNN-BiLSTM,” Computers, Materials and Continua, vol. 79, no. 3, pp. 4297–4318, 2024, doi: 10.32604/cmc.2024.050223.
U. U. Izuazu, C. I. Nwakanma, D.-S. Kim, and J. M. Lee, “Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks,” Discover Internet of Things, vol. 5, no. 1, 2025, doi: 10.1007/s43926-025-00100-0.
T. Kotsiopoulos, P. Radoglou-Grammatikis, Z. Lekka, V. Mladenov, and P. Sarigiannidis, “Defending industrial internet of things against Modbus/TCP threats: A combined AI-based detection and SDN-based mitigation solution,” Int J Inf Secur, vol. 24, no. 4, 2025, doi: 10.1007/s10207-025-01076-2.
R. K. Jha, “Strengthening Smart Grid Cybersecurity: An In-Depth Investigation into the Fusion of Machine Learning and Natural Language Processing,” Journal of Trends in Computer Science and Smart Technology, vol. 5, no. 3, pp. 284–301, 2023, doi: 10.36548/jtcsst.2023.3.005.
N. Manogaran et al., “Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security,” Sensors, vol. 25, no. 12, 2025, doi: 10.3390/s25123617.
G. Ahmadi-Assalemi, H. al-Khateeb, V. Benson, B. Adamyk, and M. Ammi, “Adaptive learning anomaly detection and classification model for cyber and physical threats in industrial control systems,” IET Cyber-Physical Systems: Theory and Applications, vol. 10, no. 1, 2025, doi: 10.1049/cps2.70004.
F. Mesadieu, D. Torre, and A. Chennamaneni, “Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA Infrastructure,” IEEE Access, vol. 12, pp. 63381–63399, 2024, doi: 10.1109/ACCESS.2024.3390722.
G. Sampedro, S. Ojo, M. Krichen, M. A. Alamro, A. Mihoub, and V. Karovič, “Defending AI Models Against Adversarial Attacks in Smart Grids Using Deep Learning,” IEEE Access, vol. 12, pp. 157408–157417, 2024, doi: 10.1109/ACCESS.2024.3473531.
E. Anthi, L. Williams, M. Rhode, P. Burnap, and A. Wedgbury, “Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems,” Journal of Information Security and Applications, vol. 58, 2021, doi: 10.1016/j.jisa.2020.102717.
A. L. Perales Gomez, L. F. Fernandez-Maimo, F. J. G. Clemente, J. A. M. Maroto, A. H. Huertas Celdrán, and G. Bovet, “A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios,” IEEE Access, vol. 10, pp. 124582–124594, 2022, doi: 10.1109/ACCESS.2022.3224930.
T. Rehman, N. Tariq, F. A. Khan, and S. U. Rehman, “FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things,” Sensors, vol. 25, no. 1, 2025, doi: 10.3390/s25010010.
A. Aldaej, T. A. Ahamed Ahanger, and I. Ullah, “Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments,” Sensors, vol. 23, no. 24, 2023, doi: 10.3390/s23249869.
I. Ahmad, Z. Wan, A. Ahmad, and S. S. Sajid Ullah, “A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things,” Mathematics, vol. 12, no. 10, 2024, doi: 10.3390/math12101437.
J. H. Lee, I. H. Ji, S. H. Jeon, and J. T. Seo, “Generating ICS Anomaly Data Reflecting Cyber-Attack Based on Systematic Sampling and Linear Regression,” Sensors, vol. 23, no. 24, 2023, doi: 10.3390/s23249855.
A. Alqudhaibi, M. Albarrak, A. Aloseel, S. Jagtap, and K. Salonitis, “Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations,” Sensors, vol. 23, no. 9, 2023, doi: 10.3390/s23094539.
Y. Ayachi, Y. Mellah, M. Saber, N. Rahmoun, I. Kerrakchou, and T. Bouchentouf, “A survey and analysis of intrusion detection models based on information security and object technology-cloud intrusion dataset,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 4, p. 1607, 2022, doi: 10.11591/ijai.v11.i4.pp1607-1614.
A. Alharthi, M. Alaryani, and S. Kaddoura, “A comparative study of machine learning and deep learning models in binary and multiclass classification for intrusion detection systems,” Array, vol. 26, p. 100406, 2025, doi: 10.1016/j.array.2025.100406.
H. Mejdi, S. Elmadssia, M. Koubãa, and T. Ezzedine, “A Comprehensive Survey on Game Theory Applications in Cyber-Physical System Security: Attack Models, Security Analyses, and Machine Learning Classifications,” IEEE Access, vol. 12, pp. 163638–163653, 2024, doi: 10.1109/ACCESS.2024.3491502.
R. Canonico and G. Sperlì, “Industrial cyber-physical systems protection: A methodological review,” Comput Secur, vol. 135, p. 103531, 2023, doi: 10.1016/j.cose.2023.103531.
T. D. Ramotsoela, G. P. Hancke, and A. M. Abu-Mahfouz, “Behavioural Intrusion Detection in Water Distribution Systems Using Neural Networks,” IEEE Access, vol. 8, pp. 190403–190416, 2020, doi: 10.1109/ACCESS.2020.3032251.
R. Holdbrook, O. Odeyomi, S. Yi, and K. Roy, “Network-Based Intrusion Detection for Industrial and Robotics Systems: A Comprehensive Survey,” Electronics (Basel), vol. 13, no. 22, p. 4440, 2024, doi: 10.3390/electronics13224440.
O. Semenenko, O. Nozdrachov, I. Chernyshova, A. Melnychenko, and D. Momot, “Innovative technologies to improve energy efficiency and security of military facilities,” Naukovij žurnal «Tehnìka ta energetika», vol. 15, no. 4, pp. 147–156, 2024, doi: 10.31548/machinery/4.2024.147.
K. Ayoub, H. Abdelmajid, T. Ayoub, and M. Soukaina, “Enhancing IoT RPL Protocol Security Against Black Hole Attacks with Deep Learning Techniques,” Journal of Computer Science, vol. 20, no. 11, pp. 1530–1544, 2024, doi: 10.3844/jcssp.2024.1530.1544.
Y. Yigit et al., “Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities,” Sensors, vol. 25, no. 6, p. 1666, 2025, doi: 10.3390/s25061666.
H. Snyder, “Literature review as a research methodology: An overview and guidelines,” J Bus Res, vol. 104, pp. 333–339, 2019, doi: 10.1016/j.jbusres.2019.07.039.
A. Carrera-Rivera, W. Ochoa, F. Larrinaga, and G. Lasa, “How-to conduct a systematic literature review: A quick guide for computer science research,” MethodsX, vol. 9, p. 101895, 2022, doi: 10.1016/j.mex.2022.101895.
K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Inf Softw Technol, vol. 64, pp. 1–18, 2015, doi: 10.1016/j.infsof.2015.03.007.
M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, pp. n71–n71, 2021, doi: 10.1136/bmj.n71.
A. H. Dehkordi, E. Mazaheri, H. A. Ibrahim, S. Dalvand, and R. Ghanei Gheshlagh, “How to Write a Systematic Review: A Narrative Review,” Int J Prev Med, vol. 12, no. 1, 2021, doi: 10.4103/ijpvm.IJPVM_60_20.
Y.-C. Chen, C.-H. Cheng, T.-W. Lin, and J.-S. Lee, “Diverse Machine Learning-Based Malicious Detection for Industrial Control System,” Electronics (Switzerland), vol. 14, no. 10, 2025, doi: 10.3390/electronics14101947.
C.-S. Shieh, T.-L. Nguyen, T.-T. Nguyen, and M.-F. Horng, “Unknown DDoS Attack Detection with Sliced Iterative Normalizing Flows Technique,” Computers, Materials and Continua, vol. 82, no. 3, pp. 4881–4912, 2025, doi: 10.32604/cmc.2025.061001.
O. Polat et al., “Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems,” Sensors, vol. 24, no. 3, 2024, doi: 10.3390/s24031040.
W. Villegas-Ch, J. Govea, A. Maldonado Navarro, and P. Palacios Játiva, “Intrusion Detection in IoT Networks Using Dynamic Graph Modeling and Graph-Based Neural Networks,” IEEE Access, vol. 13, pp. 65356–65375, 2025, doi: 10.1109/ACCESS.2025.3559325.
S. Das, M. Ashrafuzzaman, F. T. Sheldon, and S. Shiva, “Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks,” Algorithms, vol. 17, no. 3, 2024, doi: 10.3390/a17030099.
J. Zhao, P. Zeng, C. Chen, Z. Dong, and J. Han, “Deep learning anomaly detection based on hierarchical status-connection features in networked control systems,” Intelligent Automation and Soft Computing, vol. 30, no. 1, pp. 337–350, 2021, doi: 10.32604/iasc.2021.016966.
N. Yalçın, S. Çakir, and S. Ünaldı, “Attack Detection Using Artificial Intelligence Methods for SCADA Security,” IEEE Internet Things J, vol. 11, no. 24, pp. 39550–39559, 2024, doi: 10.1109/JIOT.2024.3447876.
E. Anthi, L. Williams, P. Burnap, and K. Jones, “A three-tiered intrusion detection system for industrial control systems,” J Cybersecur, vol. 7, no. 1, 2021, doi: 10.1093/cybsec/tyab006.
W. Choi, S. Pandey, and J. Kim, “Detecting Cybersecurity Threats for Industrial Control Systems Using Machine Learning,” IEEE Access, vol. 12, pp. 153550–153563, 2024, doi: 10.1109/ACCESS.2024.3478830.
M. Anwar, L. Lundberg, and A. Borg, “Improving anomaly detection in SCADA network communication with attribute extension,” Energy Informatics, vol. 5, no. 1, 2022, doi: 10.1186/s42162-022-00252-1.
S. Y. Diaba, M. Shafie-Khah, and M. Elmusrati, “On the performance metrics for cyber-physical attack detection in smart grid,” Soft comput, vol. 26, no. 23, pp. 13109–13118, 2022, doi: 10.1007/s00500-022-06761-1.
G. Raman Mr, N. Somu, and A. P. Mathur, “A multilayer perceptron model for anomaly detection in water treatment plants,” International Journal of Critical Infrastructure Protection, vol. 31, 2020, doi: 10.1016/j.ijcip.2020.100393.
S. Mubarak, M. H. Hadi Habaebi, M. R. Islam, F. D. A. Rahman, and M. Tahir, “Anomaly detection in ICS datasets with machine learning algorithms,” Computer Systems Science and Engineering, vol. 37, no. 1, pp. 33–46, 2021, doi: 10.32604/CSSE.2021.014384.
D. Agnew et al., “Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation,” Applied Sciences (Switzerland), vol. 12, no. 14, 2022, doi: 10.3390/app12146868.
S. Allah Bakhsh et al., “Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach,” IEEE Access, vol. 13, pp. 110436–110456, 2025, doi: 10.1109/ACCESS.2025.3580507.
O. Polat, A. Ayid Ahmad, S. Oyucu, E. Algül, F. Doaan, and A. Aksoz, “Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection,” IEEE Access, vol. 13, pp. 102109–102132, 2025, doi: 10.1109/ACCESS.2025.3577761.
H. Benaddi, M. Jouhari, K. Ibrahimi, J. Ben Othman, and E. M. Amhoud, “Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks,” Sensors, vol. 22, no. 21, 2022, doi: 10.3390/s22218085.
C.-J. Huang, C.-J. Chi, and W.-T. Hung, “Hybrid-AI-Based iBeacon Indoor Positioning Cybersecurity: Attacks and Defenses,” Sensors, vol. 23, no. 4, 2023, doi: 10.3390/s23042159.
A. L. P. Perales Gomez, L. F. Fernandez-Maimo, A. H. Huertas Celdrán, and F. J. G. García Clemente, “MADICS: A methodology for anomaly detection in industrial control systems,” Symmetry (Basel), vol. 12, no. 10, 2020, doi: 10.3390/SYM12101583.
M. M. Aslam, A. Tufail, L. C. De Silva, R. A. A. Haji Mohd Apong, and A. Namoun, “An improved autoencoder-based approach for anomaly detection in industrial control systems,” Systems Science and Control Engineering, vol. 12, no. 1, 2024, doi: 10.1080/21642583.2024.2334303.
B. Kim, M. A. Alawami, E. Kim, S. Oh, J. Park, and H. Kim, “A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems,” Sensors, vol. 23, no. 3, 2023, doi: 10.3390/s23031310.
V. Tkach, A. Kudin, V. R. Kebande, O. Baranovskyi, and I. Kudin, “Non-Pattern-Based Anomaly Detection in Time-Series,” Electronics (Switzerland), vol. 12, no. 3, 2023, doi: 10.3390/electronics12030721.
X. Yang, E. Howley, and M. Schukat, “ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning,” Comput Secur, vol. 141, 2024, doi: 10.1016/j.cose.2024.103825.
I. Erkek and E. Irmak, “Enhancing Cybersecurity of a Hydroelectric Power Plant Through Digital Twin Modeling and Explainable AI,” IEEE Access, vol. 13, pp. 41887–41908, 2025, doi: 10.1109/ACCESS.2025.3547672.
C. Atheeq, R. Sultana, S. A. Sabahath, and M. A. K. Mohammed, “Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems,” Engineering, Technology and Applied Science Research, vol. 14, no. 2, pp. 13559–13566, 2024, doi: 10.48084/etasr.6969.
A. Abughali, M. Alansari, and A. S. Al-Sumaiti, “Deep Learning Strategies for Detecting and Mitigating Cyber-Attacks Targeting Water-Energy Nexus,” IEEE Access, vol. 12, pp. 129690–129704, 2024, doi: 10.1109/ACCESS.2024.3458788.
P. Illy and G. Kaddoum, “A Collaborative DNN-Based Low-Latency IDPS for Mission-Critical Smart Factory Networks,” IEEE Access, vol. 11, pp. 96317–96329, 2023, doi: 10.1109/ACCESS.2023.3311822.
M. Zaman, D. Upadhyay, and C.-H. Lung, “Validation of a Machine Learning-Based IDS Design Framework Using ORNL Datasets for Power System with SCADA,” IEEE Access, vol. 11, pp. 118414–118426, 2023, doi: 10.1109/ACCESS.2023.3326751.
A. Al-Abassi, H. Karimipour, A. Dehghantanha, and R. M. Parizi, “An ensemble deep learning-based cyber-attack detection in industrial control system,” IEEE Access, vol. 8, pp. 83965–83973, 2020, doi: 10.1109/ACCESS.2020.2992249.
E. N. Yolaçan and H. Cavsi Zaim, “DCWM-LSTM: A Novel Attack Detection Framework for Robotic Arms,” IEEE Access, vol. 13, pp. 20547–20560, 2025, doi: 10.1109/ACCESS.2025.3535225.
S. Zhang, X. Xue, and X. Su, “DeepOP: A Hybrid Framework for MITRE ATT&CK Sequence Prediction via Deep Learning and Ontology,” Electronics (Switzerland), vol. 14, no. 2, 2025, doi: 10.3390/electronics14020257.
M. Lozano, I. Pérez-Llopis, and M. Esteve Domingo, “Threat Hunting Architecture Using a Machine Learning Approach for Critical Infrastructures Protection,” Big Data and Cognitive Computing, vol. 7, no. 2, 2023, doi: 10.3390/bdcc7020065.
A. Shan and S. Myeong, “Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application,” Sensors, vol. 24, no. 15, 2024, doi: 10.3390/s24154888.
D. R. Arikkat et al., “OSTIS: A novel Organization-Specific Threat Intelligence System,” Comput Secur, vol. 145, 2024, doi: 10.1016/j.cose.2024.103990.
X. Qin, F. Jiang, X. Qin, L. Ge, M. Lu, and R. Doss, “CGAN-based cyber deception framework against reconnaissance attacks in ICS,” Computer Networks, vol. 251, 2024, doi: 10.1016/j.comnet.2024.110655.
Q. Zhang, “Optimizing Threat Intelligence Strategies for Cybersecurity Awareness Using MADM and Hybrid GraphNet-Bipolar Fuzzy Rough Sets,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 11, pp. 1381–1392, 2024, doi: 10.14569/IJACSA.2024.01511136.
D. S. Morozov, A. A. Yefimenko, T. M. Nikitchuk, R. O. Kolomiiets, and S. O. Semerikov, “The sweet taste of IoT deception: an adaptive honeypot framework for design and evaluation,” Journal of Edge Computing, vol. 3, no. 2, pp. 207–223, 2024, doi: 10.55056/jec.607.
S. S. Khatami, M. Shoeibi, A. E. Oskouei, D. Martín de Andrés, and M. K. Dashliboroun, “5DGWO-GAN: A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems,” Computers, Materials and Continua, vol. 82, no. 1, pp. 881–911, 2025, doi: 10.32604/cmc.2024.059999.
Z. A. Sheikh, Y. Singh, S. Tanwar, R. Sharma, F.-E. Țurcanu, and M. S. Raboaca, “EISM-CPS: An Enhanced Intelligent Security Methodology for Cyber-Physical Systems through Hyper-Parameter Optimization,” Mathematics, vol. 11, no. 1, 2023, doi: 10.3390/math11010189.
H. Tanyıldız, C. Şahin, and Ö. Batur Dinler, “Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems,” Applied Sciences (Switzerland), vol. 14, no. 20, 2024, doi: 10.3390/app14209287.
W. Wang, F. Harrou, B. Bouyeddou, S.-M. Senouci, and Y. Sun, “A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems,” Cluster Comput, vol. 25, no. 1, pp. 561–578, 2022, doi: 10.1007/s10586-021-03426-w.
D. Fährmann, N. Damer, F. Kirchbuchner, and A. Kuijper, “Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems,” Sensors, vol. 22, no. 8, 2022, doi: 10.3390/s22082886.
J. Azar, M. Al Saleh, R. Couturier, and H. Noura, “Text Mining and Unsupervised Deep Learning for Intrusion Detection in Smart-Grid Communication Networks,” Internet of Things, vol. 6, no. 2, 2025, doi: 10.3390/iot6020022.
M. K. Ishak, “Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures,” IEEE Access, vol. 13, pp. 13329–13340, 2025, doi: 10.1109/ACCESS.2025.3530931.
H. Ünözkan, M. Ertem, and S. Bendak, “Using attack graphs to defend healthcare systems from cyberattacks: a longitudinal empirical study,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 11, no. 1, 2022, doi: 10.1007/s13721-022-00391-1.
S. Li, “Comparative Analysis of Predicting Malware Attack Trends in Cyber Supply Chain Using Multiple Classification Models,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3471802.
A. Budzys, O. Kurasova, and V. Medvedev, “Deep learning-based authentication for insider threat detection in critical infrastructure,” Artif Intell Rev, vol. 57, no. 10, 2024, doi: 10.1007/s10462-024-10893-1.
K. A. Ali Abuhasel, “A Linear Probabilistic Resilience Model for Securing Critical Infrastructure in Industry 5.0,” IEEE Access, vol. 11, pp. 80863–80873, 2023, doi: 10.1109/ACCESS.2023.3300650.
P. Lanka, K. Gupta, and C. Varol, “Intelligent Threat Detection—AI-Driven Analysis of Honeypot Data to Counter Cyber Threats,” Electronics (Switzerland), vol. 13, no. 13, 2024, doi: 10.3390/electronics13132465.
A. Salam, F. Ullah, F. Amin, and M. Abrar, “Deep Learning Techniques for Web-Based Attack Detection in Industry 5.0: A Novel Approach,” Technologies (Basel), vol. 11, no. 4, 2023, doi: 10.3390/technologies11040107.
M. M. Saeed, “An AI-Driven Cybersecurity Framework for IoT: Integrating LSTM-Based Anomaly Detection, Reinforcement Learning, and Post-Quantum Encryption,” IEEE Access, vol. 13, pp. 104027–104036, 2025, doi: 10.1109/ACCESS.2025.3576506.
S. Lehominova, Y. Shchavinsky, T. Muzhanova, D. Rabchun, and M. Zaporozhchenko, “Application of Sentiment Analysis to Prevent Cyberattacks on Objects of Critical Information Infrastructure,” International Journal of Computing, vol. 22, no. 4, pp. 534–540, 2023, doi: 10.47839/ijc.22.4.3362.
P. Radoglou-Grammatikis et al., “AI4FIDS: Multimodal Federated Intrusion Detection,” IEEE Trans Emerg Top Comput, 2025, doi: 10.1109/TETC.2025.3562346.
M. Kravchik, L. Demetrio, B. Biggio, and A. Shabtai, “Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems,” Comput Secur, vol. 122, 2022, doi: 10.1016/j.cose.2022.102901.
A. H. Oveis, G. Meucci, F. Mancuso, F. Berizzi, and A. Cantelli-Forti, “Generative AI Threats to Maritime Navigation Using Deceptive ISAR Images,” IEEE Access, vol. 12, pp. 173800–173809, 2024, doi: 10.1109/ACCESS.2024.3500774.
G. Abbas, M. Ali, M. Ahmad, and A. Khan, “CIRA-Cyber Intelligent Risk Assessment Methodology for Industrial Internet of Things Based on Machine Learning,” IEEE Access, vol. 13, pp. 77001–77016, 2025, doi: 10.1109/ACCESS.2025.3559617.
J. Vávra, M. Hromada, L. Lukáš, and J. Dworzecki, “Adaptive anomaly detection system based on machine learning algorithms in an industrial control environment,” International Journal of Critical Infrastructure Protection, vol. 34, 2021, doi: 10.1016/j.ijcip.2021.100446.
K.-K. Kim, J.-S. Kim, and I.-C. Euom, “Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems,” IEEE Access, vol. 13, pp. 66170–66187, 2025, doi: 10.1109/ACCESS.2025.3560260.
A. Al Mazroa, F. R. Albogamy, M. Ishak, and S. M. Mostafa, “Boosting Cyberattack Detection Using Binary Metaheuristics with Deep Learning on Cyber-Physical System Environment,” IEEE Access, vol. 13, pp. 11280–11294, 2025, doi: 10.1109/ACCESS.2025.3526258.
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Hak Cipta (c) 2025 Mufid Athooyaa, Satria Krisna Prabantara, Dwi Alvin Hidayat, Shaviraj Samad Shaikh, Arief Arfriandi
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