IRO Journals
Journal of Trends in Computer Science and Smart Technology

Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks

Open Access
Published: 10 July, 2026
Pages: 579-602
  • Sneha George , Sneha George
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Priscilla Joy R. , Priscilla Joy R.
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Karuppasamy K. Karuppasamy K.
    Department of Computer Science and Engineering, RVS College of Engineering and Technology, Sulur, Coimbatore, India
    Department of Computer Science and Engineering, RVS College of Engineering and Technology, Sulur, Coimbatore, India
view PDF
view PDF

How to Cite

George, Sneha, Priscilla Joy R., and Karuppasamy K. 2026. “Adversarial Attack Detection in Wireless Networks Using Deep Learning Based Capsule Networks”. Journal of Trends in Computer Science and Smart Technology 8 (3): 579-602. https://doi.org/10.36548/jtcsst.2026.3.008.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

Adversarial Attacks
Wireless Networks
Capsule Networks
Deep Learning
Dynamic Routing

Abstract

The vulnerability of wireless networks to misclassification and security oversight is increased by adversarial attacks using Deep Learning (DL)-based Intrusion Detection Systems (IDS). Because they are unable to distinguish small fluctuations in signal data and retain spatial hierarchy, traditional neural networks prefer Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are highly prone to adversarial perturbations. In this research, a Capsule Network (CapsNet)-based framework detecting adversarial attacks in wireless networks is proposed. The network uses RF-aware capsule representations and dynamic routing to improve hierarchical feature learning and robustness against adversarial perturbations. To successfully search for adversarial distortion, the proposed model extracts significant network traffic features, encodes spatial hierarchy via primary and digit capsules, and uses a reconstruction loss function. To improve the model's robustness against sophisticated attack tactics, an exploratory quantum-assisted CapsNet implementation for preliminary investigation is also included. Experimental evaluation on benchmark wireless intrusion datasets shows that CapsNets outperform conventional CNNs and Long Short-Term Memory (LSTM) models, achieving 98.3% accuracy under normal conditions and maintaining 91.3% precision even under adversarial attack, compared to 87.5% and 72.4% for CNNs and 90.2% and 78.6% for LSTMs, respectively. In addition, compared with traditional DL models, CapsNets show a 34% improvement in robustness metrics. The proposed CapsNet system achieved 98.3% detection accuracy and a reduced False Positive Rate across several adversarial attack scenarios.

References

  1. Adesina, Damilola, Chung-Chu Hsieh, Yalin E. Sagduyu, and Lijun Qian. "Adversarial Machine Learning in Wireless Communications Using RF Data: A Review." IEEE Communications Surveys & Tutorials 25, no. 1 (2022): 77-100.
  2. Mehta, Shiva, Vinay Kukreja, Ayush Dogra, and Tejinder Pal Singh Brar. "Hybrid ViT-CapsNet Framework for Brain Tumor Diagnosis Using Biomedical MRI." Biomedical and Pharmacology Journal 18, no. March Spl Edition (2025): 99-119.
  3. Muthusamy, Rajendiran, Charulatha Kannan, Jayarathna Mani, Rathinasabapathi Govindharajan, and Karthikeyan Ayyasamy. "Artificial Intelligence-Powered Intelligent Reflecting Surface Systems Countering Adversarial Attacks in Machine Learning." International Journal of Reconfigurable and Embedded Systems 13, no. 2 (2024): 414.
  4. Olutimehin, Abayomi Titilola, Adekunbi Justina Ajayi, Olufunke Cynthia Metibemu, Adebayo Yusuf Balogun, Tunboson Oyewale Oladoyinbo, and Oluwaseun Oladeji Olaniyi. "Adversarial Threats to AI-Driven Systems: Exploring the Attack Surface of Machine Learning Models and Countermeasures." Available at SSRN 5137026 (2025).
  5. Papangelo, Lorenzo, Maurizio Pistilli, Savio Sciancalepore, Gabriele Oligeri, Giuseppe Piro, and Gennaro Boggia. "Adversarial Machine Learning for Image-Based Radio Frequency Fingerprinting: Attacks and Defenses." IEEE Communications Magazine 62, no. 11 (2024): 108-113.
  6. Aiswerya, S. B., and S. Joseph Jawhar. "Detecting Complex Copy-Move Forgery Using KeyPoint-Siamese Capsule Network Against Adversarial Attacks." International Journal of Machine Learning and Cybernetics 16, no. 3 (2025): 1927-1953.
  7. Sun, Ruijin, Nan Cheng, Changle Li, Wei Quan, Haibo Zhou, Ying Wang, Wei Zhang, and Xuemin Shen. "A Comprehensive Survey of Knowledge-Driven Deep Learning for Intelligent Wireless Network Optimization in 6G." IEEE Communications Surveys & Tutorials (2025): 1099- 1135.
  8. Albalawi, Tahani, Perumal Ganeshkumar, and Faisal Albalwy. "Strategic Network Attack Prevention System Leveraging Sophisticated Query-Based Network Attention Algorithm (QNAA) And Self-Perpetuating Generative Adversarial Network (SPF-GAN) Techniques for Optimal Detection." Electronics 14, no. 5 (2025): 922.
  9. Rezaei, Hadiseh, Rahim Taheri, and Mohammad Shojafar. "FedLLMGuard: A Federated Large Language Model for Anomaly Detection In 5G Networks." Computer Networks 269 (2025): 111473.
  10. Ayanoglu, Ender, Kemal Davaslioglu, and Yalin E. Sagduyu. "Machine Learning In NextG Networks via Generative Adversarial Networks." IEEE Transactions on Cognitive Communications and Networking 8, no. 2 (2022): 480-501.
  11. Bamber, Sukhvinder Singh, Aditya Vardhan Reddy Katkuri, Shubham Sharma, and Mohit Angurala. "A Hybrid CNN-LSTM Approach for Intelligent Cyber Intrusion Detection System." Computers & Security 148 (2025): 104146.
  12. Yu, JianTong, and Li Li. "Research on 5G User Perception Detection and Experience Improvement Optimization Based on Capsule Network." International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 16, no. 1 (2024): 1-20.
  13. Li, Yueqiao, Hang Su, and Jun Zhu. "AdvCapsNet: To Defense Adversarial Attacks Based on Capsule Networks." Journal of Visual Communication and Image Representation 75 (2021): 103037.
  14. Gufran, Danish, Pooja Anandathirtha, and Sudeep Pasricha. "Sentinel: Securing indoor Localization Against Adversarial Attacks with Capsule Neural Networks." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 43, no. 11 (2024): 4021-4032.
  15. Alsharaiah, Mohammad A., Mohammed Amin Almaiah, Mansour Obeidat, and Rami Shehab. "Capsule Network Model for Detecting Spoofing Attack in the Internet of Medical Things (IoMT)." Iraqi Journal for Computer Science and Mathematics 6, no. 3 (2025): 35.
  16. Zanini, Samuele, Stefania Bartoletti, Giulia Focarelli, Ivan Palama, and Giuseppe Bianchi. "Location Security in 5G and Beyond: Potential Threats and Countermeasures." IEEE Communications Magazine (2026).
  17. Alla, Ildi, and Valeria Loscri. "Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture." In 2025 IEEE Conference on Communications and Network Security (CNS), IEEE, 2025, 1-9.
  18. Wang, Yulong, Tong Sun, Shenghong Li, Xin Yuan, Wei Ni, Ekram Hossain, and H. Vincent Poor. "Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey." IEEE Communications Surveys & Tutorials 25, no. 4 (2023): 2245-2298.
  19. Ilahi, Inaam, Muhammad Usama, Junaid Qadir, Muhammad Umar Janjua, Ala Al-Fuqaha, Dinh Thai Hoang, and Dusit Niyato. "Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning." IEEE Transactions on Artificial Intelligence 3, no. 2 (2021): 90-109.
  20. Sreekala, Keshetti, C. Pretty Diana Cyril, S. Neelakandan, Saravanan Chandrasekaran, Ranjan Walia, and Eric Ofori Martinson. "Capsule Network‐Based Deep Transfer Learning Model for Face Recognition." Wireless Communications and Mobile Computing 2022, no. 1 (2022): 2086613.
  21. Khaleel, Yahya Layth, Mustafa Abdulfattah Habeeb, Ahmed Shihab Albahri, Tahsien Al-Quraishi, Osamah Shihab Albahri, and Abdullah Hussein Alamoodi. "Network and Cybersecurity Applications of Defense in Adversarial Attacks: A State-of-the-Art Using Machine Learning and Deep Learning Methods." Journal of Intelligent Systems 33, no. 1 (2024): 20240153.
  22. Sasi, Tinshu, Arash Habibi Lashkari, Rongxing Lu, Pulei Xiong, and Shahrear Iqbal. "An Efficient Self Attention-Based 1D-CNN-LSTM Network for IoT Attack Detection and Identification Using Network Traffic." Journal of Information and Intelligence (2024): 375-400.
  23. Li, Xingwang, Yuan Gao, Ming Zeng, Xianfu Lei, Wanming Hao, Arumugam Nallanathan, and Octavia A. Dobre. "Recent Advances in Resource Allocation and Beam Prediction for Large Language Models Empowered ISAC Systems." IEEE Communications Magazine (2026) 113-119.
  24. B. Kim, Y. E. Sagduyu, K. Davaslioglu, T. Erpek and S. Ulukus, "Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers," in IEEE Transactions on Wireless Communications, vol. 21, no. 6, June 2022, 3868-3880.
  25. Shihab, Mustafa Abdmajeed, Haydar Abdulameer Marhoon, Saadaldeen Rashid Ahmed, Ahmed Dheyaa Radhi, and Ravi Sekhar. "Towards Resilient Machine Learning Models: Addressing Adversarial Attacks in Wireless Sensor Network." Journal of Robotics and Control (JRC) 5, no. 5 (2024): 1599-1617.
  26. Zhang, Chaoyun, Paul Patras, and Hamed Haddadi. "Deep Learning in Mobile and Wireless Networking: A Survey." IEEE Communications surveys & tutorials 21, no. 3 (2019): 2224-2287.
  27. Parasuraman, Manikandan, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi, and Tahani Jaser Alahmadi. "An Optimized Transmission Mechanism for Mitigating Jamming Attacks in Multi-Hop Wireless Networks." International Journal of Sensors, Wireless Communications and Control 15, no. 2 (2025): 117-132.
  28. Son, Bui Duc, Gaosheng Zhao, Trinh Van Chien, and Dong In Kim. "Secure Distributed RIS-MIMO Over Double Scattering Channels: Adversarial Attack, Defense, and SER Improvement." IEEE Transactions on Communications 74 (2025): 797-811.
  29. Zhang, Dingwen, Liangbo Cheng, Yi Liu, Xinggang Wang, and Junwei Han. "Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection." International Journal of Computer Vision 133, no. 10 (2025): 7201-7221.
  30. Zahid, Mohammad, and Taran Singh Bharati. "Leveraging Machine Learning and Deep Learning in IoT Security: A Review." Security and Privacy 9, no. 1 (2026): e70144.
  31. Blessie, E. Chandra, Pethuru Raj, and B. Sundaravadivazhagan, eds. Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks. CRC Press, 2026.
  32. Ieropoulos, Vasilis, Eirini Anthi, Theodoros Spyridopoulos, Pete Burnap, Pietro Carnelli, and Aftab Khan. "Federated Detection at The Edge: Collaborative Anomaly Detection for Resource-Limited IoT." IEEE Internet of Things Journal (2026): 24205- 24218.
  33. Muthusundar, S. K., Nallusamy R, Shri Vindhya A, and Vignesh T. "Wireless Sensor Networks Security Using SACCGAN‐RSA in a Novel Deep Learning–Based Intrusion Detection Framework for Enhancing." International Journal of Communication Systems 39, no. 4 (2026): e70397.
  34. Askhatuly, Aidos, Dinara Berdysheva, Azamat Berdyshev, Aigul Adamova, and Didar Yedilkhan. "Adversarial Attacks and Defense Mechanisms in Machine Learning: A Structured Review of Methods, Domains, and Open Challenges." IEEE Access (2025).
  35. 5G/6G Multipath Fading Equalization Dataset, Available online at: https://www.kaggle.com/datasets/programmer3/5g6g-multipath-fading-equalization-dataset, Last accessed on 31st May 2026.
  36. 6G Network Slice Security Attack Detection, Available online at: https://www.kaggle.com/datasets/ziya07/6g-network-slice-security-attack-detection, Last accessed on 31st May 2026.