A novel AI-driven intrusion detection framework for cloud-IoT sensor networks

Maryam Mahdi Alhusseini; Inam Abdullah Lafta; Fatima Faydhe Al-Azzaw; Fadhil Abbas Fadhil; Mohammad-Reza Feizi-Derakhshi; Azhar Naji Alyahya; Zaid Ameen Abduljabbar; Vincent Omollo Nyangaresi

doi:10.1016/j.jnlest.2026.100375

Journal of Electronic Science and Technology ›› 2026, Vol. 24 ›› Issue (3) :100375 DOI: 10.1016/j.jnlest.2026.100375

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A novel AI-driven intrusion detection framework for cloud-IoT sensor networks

Author information +

¹ Information and Communication Technology Department, Polytechnic College of Engineering – Baghdad, Middle Technical University, Baghdad, 10011, Iraq

² Electronics Engineering and AI Technologies Department, Polytechnic College of Engineering – Baghdad, Middle Technical University, Baghdad, 10011, Iraq

³ College of Computer Science, University of Technology- Iraq, Baghdad, 10066, Iraq

⁴ Department of Computer Engineering, University of Tabriz, Tabriz, 5166616471, Iran

⁵ Department of Software Engineering, Sakarya University, Sakarya, 54050, Türkiye

⁶ Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, 61004, Iraq

⁷ Shenzhen Institute, Huazhong University of Science and Technology, Shenzhen, 518000, China

⁸ Department of Computer Science and Software Engineering, Jaramogi Oginga Odinga University of Science&Technology, Bondo, 40601, Kenya

⁹ Department of Applied Electronics, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, 602105, India

^* E-mail addresses: mariammahdi@mtu.edu.iq (M.M. Alhusseini),

inamabdullah96@mtu.edu.iq (I.A. Lafta),

fatima_faydy1981@mtu.edu.iq (F.F. Al-Azzaw),

fadhil.a.fadhil@uotechnology.edu.iq (F.A. Fadhil),

mfeizi@tabrizu.ac.ir (M.-R. Feizi-Derakhshi),

azhar.alyahya@ogr.sakarya.edu.tr (A.N. Alyahya),

zaid.ameen@uobasrah.edu.iq (Z.A. Abduljabbar),

vnyangaresi@jooust.ac.ke (V.O. Nyangaresi).

Maryam Mahdi Alhusseini received the B.S. degree in computer engineering from Middle Technical University (MTU), Baghdad, Iraq in 2007, and the M.S. degree in computer engineering (software engineering–cybersecurity using AI) from the Azerbaijan Shahid Madani University (ASMU), Tabriz, Iran in 2023. She is a Lecturer with the Information and Communication Technology Department, Polytechnic College of Engineering–Baghdad, MTU. She is a member of the IEEE and serves as a reviewer for several peer-reviewed journals indexed in Scopus and Clarivate, as well as international conferences. She is also an Elsevier-recognized reviewer and has received official certification for her peer-review contributions. Her research interests include applied artificial intelligence (AI), cybersecurity, optimization algorithms, cloud computing (CC), Internet of things (IoT), natural language processing (NLP), and sentiment analysis

Inam Abdullah Lafta received the M.S. degree in information technology from Universiti Utara Malaysia (UUM), Kedah, Malaysia in 2016. She is currently a Lecturer with the Information and Communication Technology (ICT) Department, Polytechnic College of Engineering–Baghdad, MTU. She has published several research papers and has held various administrative positions, including the Head of the ICT Department at the Polytechnic College of Engineering, Baghdad, MTU. Her research interests include AI, software requirements engineering, computer system safety, cryptography, medical imaging, and digital steganography

Fatima Faydhe Al-Azzawi received the M.S. degree in communications engineering from the University of Technology–Iraq, Baghdad, Iraq in 2005. She is currently an Assistant Professor with the Electronics Engineering and AI Technologies Department, Polytechnic College of Engineering–Baghdad, MTU. Her research interests include communication engineering, wireless communication, networking, and modulation

Fadhil Abbas Fadhil received the M.S. degree in computer science (cybersecurity) from Ural Federal University (UrFU), Yekaterinburg, Russia in 2022. He is currently a Lecturer with the College of Computer Science, University of Technology–Iraq, Baghdad, Iraq, where he also served as the Head of the IT Division. His research focuses on data and multimedia security, neural network-based encryption optimization, secure information embedding in video streams, and AI applications in cybersecurity

Mohammad-Reza Feizi-Derakhshi received the B.S. degree in software engineering from the University of Isfahan, Isfahan, Iran in 1997, and the M.S. and Ph.D. degrees in artificial intelligence from the Iran University of Science and Technology, Tehran, Iran in 2000 and 2007, respectively. He is currently a full-time Professor with the University of Tabriz, Tabriz, Iran. His research interests include NLP, optimization algorithms, social network analysis, and intelligent databases

Azhar Naji Alyahya received the M.S. degree from Sakarya University, Sakarya, Türkiye in 2024. She is currently a Lecturer with the Department of Software Engineering, Sakarya University. Her research interests include applied AI, optimization algorithms, and machine learning

Zaid Ameen Abduljabbar received the B.S. and M.S. degrees in computer science from the University of Basrah, Basrah, Iraq in 2002 and 2006, respectively, and the Ph.D. degree in computer engineering from Shenzhen Institute, Huazhong University of Science and Technology, Shenzhen, China in 2017. He is currently a Lecturer and Researcher with the Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq. He has published regular articles in more than 100 IEEE and Springer international conferences and articles in SCI journals, and holds eight international patents and two international computer software copyrights. His research interests include cloud security, searchable encryption systems, similarity measures, IoT, secure computation, biometrics, and soft computing

Vincent Omollo Nyangaresi received the B.S. degree in telecommunications & information technology and the M.S. and Ph.D. degrees in information technology security & audit from Jaramogi Oginga Odinga University of Science & Technology, Bondo, Kenya in 2010, 2018, and 2022, respectively. He is currently serving as the Chair of the Department of Computer Science and Software Engineering, Jaramogi Oginga Odinga University of Science & Technology. He is an adjunct faculty member at the Department of Applied Electronics, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India. He is an experienced researcher in the areas of computer science and information technology and has published over 200 research articles in peer-reviewed journals and conferences. He is ranked among the top 2% scientists in the world in information and communication technology by Stanford/Elsevier. He is currently serving as an Editor in top journals. He has served as a Guest Editor in top journals. Moreover, he is a renowned reviewer for more than 120 journals in IEEE, IGI, Taylor & Francis, MDPI, PLOS ONE, Elsevier, and Springer. He has served as a Technical Committee Member (TPC) for numerous international conferences and symposia. His research interests include AI, machine learning, wireless network security, quantum computing, IoT, wireless sensor networks (WSNs), cellular networks, information systems acceptance modeling, TCP architecture and design, radio wave propagation, virtualization, and CC.

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Abstract

Securing network infrastructure against cyberattacks is essential in rapidly evolving digital landscapes. This paper proposes an AI-based real-time intrusion detection system (IDS) for wireless sensor network (WSN), cloud computing (CC), and Internet of things (IoT) environments. The framework employs classical machine learning (ML) models, including decision trees (DTs), K-nearest neighbors (KNN), and logistic regression, optimized using the energy valley optimization (EVO) algorithm on the Network Security Laboratory − Knowledge Discovery in Databases (NSL-KDD) dataset. Feature selection (FS) reduced the number of input features from 42 to 18 without compromising the detection performance. The system achieved high accuracy rates of 98.95% with a decision tree, 98.47% with KNN, and 88.84% with logistic regression, along with high precision, recall, and F1-scores. In addition, the training and testing times were significantly reduced, supporting real-time deployment. Class imbalance was addressed through downsampling to ensure balanced detection across attack types. The proposed model offers a scalable, low-latency, and accurate intrusion detection solution aligned with modern cybersecurity requirements.

Keywords

Cloud computing / Feature selection (FS) strategy / Internet of things (IoT) / Intelligent network intrusion detection system (NIDS) / Network detection / Novel optimization / Sampling technique / Wireless sensor network (WSN)

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Maryam Mahdi Alhusseini, Inam Abdullah Lafta, Fatima Faydhe Al-Azzaw, Fadhil Abbas Fadhil, Mohammad-Reza Feizi-Derakhshi, Azhar Naji Alyahya, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi. A novel AI-driven intrusion detection framework for cloud-IoT sensor networks. Journal of Electronic Science and Technology, 2026, 24 (3) : 100375 DOI:10.1016/j.jnlest.2026.100375

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