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TransMalDE: An Effective Transformer Based Hierarchical Framework for IoT Malware Detection

Deng, X., Wang, Z., Pei, X., & Xue, K. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3292855 · Cited by 32 · see citing works

Need of UAVs and Physical Layer Security in Next-Generation Non-Terrestrial Wireless Networks: Potential Challenges and Open Issues

Haq, A. U., Sefati, S. S., Nawaz, S. J., Mihovska, A., & Beliatis, M. J. (2025) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2025.3525781 · Cited by 33 · see citing works

An Optimized Deep Learning Based Security Enhancement and Attack Detection on IoT Using IDS and KH-AES for Smart Cities

DURAISAMY, A., Subramaniam, M., & Robin, C. R. R. (2021) · Studies in Informatics and Control · doi:10.24846/v30i2y202111 · Cited by 31 · see citing works

Data-driven Curation, Learning and Analysis for Inferring Evolving IoT Botnets in the Wild

Pour, M. S., Mangino, A., Friday, K., Rathbun, M., Bou‐Harb, E., Iqbal, F., Shaban, K. B., & Erradi, A. (2019) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3339252.3339272 · Cited by 30 · see citing works

Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications

Ali, S., Ferdowsi, A., Saad, W., & Rajatheva, N. (2018) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2018.8644350 · Cited by 29 · see citing works

On the use of artificial intelligence to deal with privacy in IoT systems: A systematic literature review

Giordano, G., Palomba, F., & Ferrucci, F. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111475 · Cited by 28 · see citing works

Information Security Methods—Modern Research Directions

Shelupanov, A. A., Evsutin, O. O., Konev, A. A., Kostyuchenko, E. Y., Kruchinin, D., & Сергеевич, Н. Д. (2019) · Symmetry · doi:10.3390/sym11020150 · Cited by 28 · see citing works

Dynamic distributed generative adversarial network for intrusion detection system over internet of things

Balaji, S. G., & Narayanan, S. S. (2022) · Wireless Networks · doi:10.1007/s11276-022-03182-8 · Cited by 26 · see citing works

RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth

Jagannath, A., Kane, Z., & Jagannath, J. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001572 · Cited by 26 · see citing works

The Use of the Blockchain Technology and Digital Watermarking to Provide Data Authenticity on a Mining Enterprise

Evsutin, O. O., & Meshcheryakov, Y. (2020) · Sensors · doi:10.3390/s20123443 · Cited by 25 · see citing works

DeepEnc: deep learning-based CT image encryption approach

Abdellatef, E., Naeem, E. A., & El‐Samie, F. E. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15818-8 · Cited by 25 · see citing works

Colonel Blotto Game for Secure State Estimation in Interdependent Critical InfrastructurePreprint

Ferdowsi, A., Saad, W., & Mandayam, N. B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.09768 · Cited by 25 · see citing works

Colonel Blotto Game for Sensor Protection in Interdependent Critical Infrastructure

Ferdowsi, A., Saad, W., & Mandayam, N. B. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3020901 · Cited by 24 · see citing works

Efficient Security Algorithm for Power-Constrained IoT Devices

Mamvong, J. N., Goteng, G. L., Zhou, B., & Gao, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3033435 · Cited by 27 · see citing works

Blockchain for Cyber-Physical Systems: Challenges and Applications

Maleh, Y., Lakkineni, S., Tawalbeh, L., & El‐Latif, A. A. A. (2022) · Internet of things · doi:10.1007/978-3-030-93646-4_2 · Cited by 28 · see citing works

Potential technologies and applications based on deep learning in the 6G networks

Zheng, Z., Wang, L., Zhu, F., & Liu, L. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107373 · Cited by 25 · see citing works

Advances on networked ehealth information access and sharing: Status, challenges and prospects

Aski, V. J., Dhaka, V. S., Kumar, S., Verma, S., & Rawat, D. B. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108687 · Cited by 23 · see citing works

Towards a Secure and Scalable IoT Infrastructure: A Pilot Deployment for a Smart Water Monitoring System

Overmars, A., & Venkatraman, S. (2020) · Technologies · doi:10.3390/technologies8040050 · Cited by 22 · see citing works

A survey on security issues of 5G NR: Perspective of artificial dust and artificial rain

Shafi, M., Jha, R. K., & Sabraj, M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102597 · Cited by 21 · see citing works

On Self-Security of Grid-Interactive Smart Inverters

Gursoy, M., & Mirafzal, B. (2021) · IEEE Kansas Power and Energy Conference (KPEC) · doi:10.1109/kpec51835.2021.9446213 · Cited by 21 · see citing works

Artificial Intelligence for Wireless Connectivity and Security of Cellular-Connected UAVs.Preprint

Challita, U., Ferdowsi, A., Chen, M., & Saad, W. (2018) · arXiv (Cornell University) · Cited by 20 · see citing works

Design Guidelines for Machine Learning-based Cybersecurity in Internet of Things

Boukerche, A., & Coutinho, R. W. L. (2020) · IEEE Network · doi:10.1109/mnet.011.2000396 · Cited by 19 · see citing works

Information Security Monitoring and Management Method Based on Big Data in the Internet of Things Environment

Liang, W., Wen-ning, L., & Feng, L. (2021) · IEEE Access · doi:10.1109/access.2021.3064350 · Cited by 19 · see citing works

RLAuth: A Risk-Based Authentication System Using Reinforcement Learning

Picard, C., & Pierre, S. (2023) · IEEE Access · doi:10.1109/access.2023.3286376 · Cited by 17 · see citing works

Comm-Transformer: A Robust Deep Learning-Based Receiver for OFDM System Under TDL Channel

Xie, Y., Teh, K. C., & Kot, A. C. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3343787 · Cited by 17 · see citing works

A Stochastic Bayesian Game for Securing Secondary Frequency Control of Microgrids Against Spoofing Attacks With Incomplete Information

Li, Y., Liu, S., & Zhu, L. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3396106 · Cited by 17 · see citing works

On securing IoT from Deep Learning perspective

Otoum, Y., & Nayak, A. R. (2020) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc50000.2020.9219671 · Cited by 17 · see citing works

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2018) · arXiv · doi:10.48550/arxiv.1810.07862 · Cited by 17 · see citing works

Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over Sensor Networks

Huang, F., Zhang, J., Zhang, S., Chen, H., & Poor, H. V. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3100624 · Cited by 16 · see citing works

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics

Xiang, X., Foo, S. Y., & Zang, H. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040043 · Cited by 13 · see citing works

Smart Inverter Twin Model for Anomaly Detection

Hossen, T., Sharma, D., & Mirafzal, B. (2021) · IEEE ... Workshop on Control and Modeling for Power Electronics · doi:10.1109/compel52922.2021.9646038 · Cited by 13 · see citing works

A Review on Deep Reinforcement Learning for the management of SDN and NFV in Edge-IoT

Alonso, R. S., Prieto, J., la Prieta, F. D., Rodrı́guez, S., & Corchado, J. M. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682179 · Cited by 13 · see citing works

IoT Security Vulnerabilities and Defensive Measures in Industry 4.0

Koppula, M., & Joseph, L. M. I. L. (2023) · Advanced technologies and societal change · doi:10.1007/978-981-99-2115-7_4 · Cited by 13 · see citing works

Survey on the application of deep learning in the Internet of Things

Shadroo, S., Rahmani, A. M., & Rezaee, A. (2022) · Telecommunication Systems · doi:10.1007/s11235-021-00870-2 · Cited by 14 · see citing works

Intrusion detection system using distributed multilevel discriminator in GAN for IoT system

Poongodi, M., & Hamdi, M. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4815 · Cited by 12 · see citing works

Self-Security for Grid-Interactive Smart Inverters Using Steady-State Reference Model

Gursoy, M., & Mirafzal, B. (2021) · IEEE ... Workshop on Control and Modeling for Power Electronics · doi:10.1109/compel52922.2021.9645993 · Cited by 12 · see citing works

A blockchain-enabled edge computing framework leveraging artificial neural network and aquila optimization to enhance security and scalability of cloud-based IoT platforms

Hazber, M. A. G., Albarrak, A., Altamimi, M., Muniasamy, A., Islam, A., Ahmed, M. A., Alalayah, K. M. A., Hussain, S., & Irshad, R. R. (2025) · Cluster Computing · doi:10.1007/s10586-025-05518-3 · Cited by 11 · see citing works

Classification and Analysis of Adversarial Machine Learning Attacks in IoT: a Label Flipping Attack Case Study

Abrishami, M., Dadkhah, S., Neto, E. C. P., Xiong, P., Iqbal, S., Ray, S., & Ghorbani, A. A. (2022) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct56874.2022.9953823 · Cited by 13 · see citing works

Enhancing IoT privacy with artificial intelligence: Recent advances and future directions

Tsouplaki, A., Fung, C., & Kalloniatis, C. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101752 · Cited by 12 · see citing works

Prenatal Healthcare Framework Using IoMT Data Analytics

Pandey, R., Pandey, A., Maurya, P., & Singh, G. D. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-3533-5.ch004 · Cited by 10 · see citing works

Maximizing IoT Security: An Examination of Cryptographic Algorithms

Arya, L., Sharma, Y. K., Kumar, R., Padmanaban, H., Devi, S., & Tyagi, L. K. (2023) · International Conference on Power Energy, Environment and Intelligent Control (PEEIC) · doi:10.1109/peeic59336.2023.10451210 · Cited by 10 · see citing works

Enhancing Differential Testing with LLMs for Testing Deep Learning Libraries

Li, M., Li, D., Liu, J., Cao, J., Tian, Y., & Cheung, S.-C. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3735637 · Cited by 13 · see citing works

Overview of Information Hiding Algorithms for Ensuring Security in IoT Based Cyber-Physical Systems

Evsutin, O. O., Сергеевна, М. А., & El‐Latif, A. A. A. (2021) · Studies in big data · doi:10.1007/978-3-030-85428-7_5 · Cited by 10 · see citing works

Cyber-Physical Security and Safety of Autonomous Connected Vehicles: Optimal Control Meets Multi-Armed Bandit Learning

Ferdowsi, A., Ali, S., Saad, W., & Mandayam, N. B. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2927570 · Cited by 8 · see citing works

Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offsPreprint

Rangi, A., Khojasteh, M. J., & Franceschetti, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10718 · Cited by 8 · see citing works

TXAI-ADV: Trustworthy XAI for Defending AI Models against Adversarial Attacks in Realistic CIoT

Ojo, S., Krichen, M., Alamro, M. A., & Mihoub, A. (2024) · Electronics · doi:10.3390/electronics13091769 · Cited by 8 · see citing works

Deep Learning Approaches for IoT Security in the Big Data Era

Krishnan, K. S. S., & Thampi, S. M. (2020) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-030-35642-2_6 · Cited by 7 · see citing works

Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Ferdowsi, A., & Saad, W. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422728 · Cited by 78 · see citing works

Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication

Korde, M., Gupta, V., Mandloi, A., Puntambekar, S., & Bais, D. S. (2024) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.120109

A Deep Learning and Channel Sounding Based Data Authentication and QoS Enhancement Mechanism for Massive IoT Networks

Kumar, R., Joshi, G., Chauhan, A. K. S., Singh, A. K., & Rao, A. K. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10389-1 · Cited by 3 · see citing works

Deep Learning-based Continuous Authentication for an IoT-enabled healthcare service

Sahu, A. K., Sharma, S. K., & Raja, R. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107817 · Cited by 32 · see citing works

A Security Framework for Internet of Things Systems Based on Dynamic Watermarking for Data Packet Authentication and Anomaly Detection

Gu, L. (2023) · doi:10.1109/ieem58616.2023.10406522