A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy

Momand, Asadullah; Jan, Sana Ullah; Ramzan, Naeem

doi:10.1155/2023/6048087

Generative Adversarial Networks-enabled Anomaly Detection Systems: A Survey (2025)
Journal Article
Saeed, U., Jan, S. U., Ahmad, J., Shah, S. A., Alshehri, M. S., Ghadi, Y. Y., Pitropakis, N., & Buchanan, W. J. (online). Generative Adversarial Networks-enabled Anomaly Detection Systems: A Survey. Expert Systems with Applications, 296(B), Article 128978. https://doi.org/10.1016/j.eswa.2025.128978

Anomaly Detection (AD) is an important area of research because it helps identify outliers in data, enabling early detection of errors, fraud, and potential security breaches. Machine Learning (ML) can be utilized for distinct AD systems, and Generat... Read More about Generative Adversarial Networks-enabled Anomaly Detection Systems: A Survey.

Cross-Platform Benchmarking of the FHE Libraries: Novel Insights into SEAL and OpenFHE (2025)
Presentation / Conference Contribution
Faneela, Ahmad, J., Ghaleb, B., Jan, S. U., & Buchanan, W. J. (2025, April). Cross-Platform Benchmarking of the FHE Libraries: Novel Insights into SEAL and OpenFHE. Presented at 2025 International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan

The rapid growth of cloud computing and data-driven applications has amplified privacy concerns, driven by the increasing demand to process sensitive data securely. Homomorphic encryption (HE) enables computation on encrypted data while maintaining p... Read More about Cross-Platform Benchmarking of the FHE Libraries: Novel Insights into SEAL and OpenFHE.

A Blockchain-Based Secure Data-Sharing Framework for Automotive Supply Chains: Enhancing Privacy with Attribute-Based Encryption (2025)
Presentation / Conference Contribution
Ahmed, E., Ghaleb, B., Abubakar, M., & Jan, S. U. (2025, April). A Blockchain-Based Secure Data-Sharing Framework for Automotive Supply Chains: Enhancing Privacy with Attribute-Based Encryption. Presented at 2025 International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan

Original Equipment Manufacturers (OEMs) encounter significant challenges in facilitating secure and efficient data sharing, as they must strike a delicate balance between maintaining stringent data confidentiality and enabling seamless collaboration... Read More about A Blockchain-Based Secure Data-Sharing Framework for Automotive Supply Chains: Enhancing Privacy with Attribute-Based Encryption.

Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques (2025)
Journal Article
Konstantinou, A., Kasimatis, D., Buchanan, W. J., Ullah Jan, S., Ahmad, J., Politis, I., & Pitropakis, N. (2025). Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques. Machine Learning and Knowledge Extraction, 7(2), Article 31. https://doi.org/10.3390/make7020031

This paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend... Read More about Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques.

ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks (2024)
Journal Article
Momand, A., Jan, S. U., & Ramzan, N. (in press). ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks. Wireless Personal Communications, 136(4), 1981-2003. https://doi.org/10.1007/s11277-024-11260-7

This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed ABCNN employs an attention mechanism that aids in the learning process for low-instance classes. On the... Read More about ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks.

Transfer Learning with Domain Adaptation for Unlabelled Sensor Faulty Data Classification (2024)
Presentation / Conference Contribution
Hasan, M. N., Toma, R. N., Jan, S. U., & Koo, I. (2024, October). Transfer Learning with Domain Adaptation for Unlabelled Sensor Faulty Data Classification. Presented at 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea

In the data analytics based IoT era, reliable sensor data is essential for maintaining the integrity and effectiveness of IoT applications. The reliability of sensor data can be compromised due to faults that that occur in the sensors and this faulty... Read More about Transfer Learning with Domain Adaptation for Unlabelled Sensor Faulty Data Classification.

Advances in Multi‐modal Remote Infant Monitoring Systems (2024)
Book Chapter
Saher, N., Riaz, O., Suleman, M., Khan, D. M., Kirn, N., Jan, S. U., Shahid, R., Rabah, H., & Ramzan, N. (2025). Advances in Multi‐modal Remote Infant Monitoring Systems. In M. Ur Rehman, A. Zoha, M. Ali Jamshed, & N. Ramzan (Eds.), Multimodal Intelligent Sensing in Modern Applications (227-251). Wiley. https://doi.org/10.1002/9781394257744.ch10

This chapter offers a thorough analysis of recent research on remote patient monitoring (RPM) from the perspective of modified sensing technologies using both contact-based and contact-less sensors. It examines the latest advancements in remote monit... Read More about Advances in Multi‐modal Remote Infant Monitoring Systems.

Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder (2024)
Journal Article
Hasan, M. N., Jan, S. U., & Koo, I. (2024). Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder. Applied Sciences, 14(17), Article 7717. https://doi.org/10.3390/app14177717

The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applicatio... Read More about Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder.

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT) (2024)
Journal Article
Shah, S. M. A. H., Jan, S. U., Khan, M. Q., Rizwan, A., Samee, N. A., & Jamjoom, M. M. (2024). Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT). Pattern Analysis and Applications, 27, Article 76. https://doi.org/10.1007/s10044-024-01297-6

Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using ar... Read More about Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT).

ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT (2024)
Presentation / Conference Contribution
Huma, Z. E., Ahmad, J., Hamadi, H. A., Ghaleb, B., Buchanan, W. J., & Jan, S. U. (2024, February). ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT. Presented at 2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates

The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them... Read More about ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT.

Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype (2024)
Journal Article
Khan, S. U., Ullah Jan, S., Hwang, T., & Koo, I.-S. (2024). Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype. Bulletin of Electrical Engineering and Informatics, 13(2), 1400-1410. https://doi.org/10.11591/eei.v13i2.5309

E-health is being adapted in modern hospitals as a significant addition to the existing healthcare services. To this end, modern hospitals urgently require a mobile, high-capacity, secure, and cost-effective communication infrastructure. In this pape... Read More about Hybrid Wi-Fi and PLC network for efficient e-health communication in hospitals: a prototype.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images (2023)
Journal Article
Khan, S. U., Jan, S. U., & Koo, I. (2023). Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images. Sensors, 23(23), Article 9572. https://doi.org/10.3390/s23239572

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Trad... Read More about Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images.

A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things (2023)
Presentation / Conference Contribution
Momand, A., Jan, S. U., & Ramzan, N. (2023, May). A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things. Presented at ISPR'2023: The International Conference on Intelligent Systems and Pattern Recognition, Hammamet, Tunisia

The Internet of Things (IoT) is the connection of smart devices and objects to the internet, allowing them to share and analyze data, communicate with each other, and be controlled remotely. Several IoT devices are designed to collect, process, and s... Read More about A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things.

TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks (2023)
Journal Article
Ullah, S., Ahmad, J., Khan, M. A., Alshehri, M. S., Boulila, W., Koubaa, A., Jan, S. U., & Iqbal Ch, M. M. (2023). TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks. Computer Networks, 237, Article 110072. https://doi.org/10.1016/j.comnet.2023.110072

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart device... Read More about TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture (2023)
Journal Article
Masood, F., Khan, W. U., Jan, S. U., & Ahmad, J. (2023). AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture. Sensors, 23(19), Article 8218. https://doi.org/10.3390/s23198218

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature,... Read More about AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data (2023)
Presentation / Conference Contribution
Naz, N., Khan, M. A., Khan, M. A., Khan, M. A., Jan, S. U., Shah, S. A., Arshad, Abbasi, Q. H., & Ahmad, J. (2022, October). A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data. Presented at 3rd International Conference of Advanced Computing and Informatics, Casablanca, Morocco

The Internet of Things (IoT) is a grid of interconnected pre-programmed electronic devices to provide intelligent services for daily life tasks. However, the security of such networks is a considerable obstacle to successful implementation. Therefore... Read More about A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data.

A Hybrid Neuro-Fuzzy Approach for Heterogeneous Patch Encoding in ViTs Using Contrastive Embeddings and Deep Knowledge Dispersion (2023)
Journal Article
Shah, S. M. A. H., Khan, M. Q., Ghadi, Y. Y., Jan, S. U., Mzoughi, O., & Hamdi, M. (2023). A Hybrid Neuro-Fuzzy Approach for Heterogeneous Patch Encoding in ViTs Using Contrastive Embeddings and Deep Knowledge Dispersion. IEEE Access, 11, 83171-83186. https://doi.org/10.1109/access.2023.3302253

Vision Transformers (ViT) are commonly utilized in image recognition and related applications. It delivers impressive results when it is pre-trained using massive volumes of data and then employed in mid-sized or small-scale image recognition evaluat... Read More about A Hybrid Neuro-Fuzzy Approach for Heterogeneous Patch Encoding in ViTs Using Contrastive Embeddings and Deep Knowledge Dispersion.

Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks (2023)
Journal Article
Hasan, M. N., Jan, S. U., & Koo, I. (2023). Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks. IEEE Sensors Journal, 23(12), 13327-13339. https://doi.org/10.1109/JSEN.2023.3272908

In this Internet of Things (IoT) era, the number of devices capable of sensing their surroundings is increasing day by day. Based on the data from these devices, numerous services and systems are now offered where critical decisions depend on the dat... Read More about Wasserstein GAN-based Digital Twin Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks.

BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things (2023)
Journal Article
Badri, S., Ullah Jan, S., Alghazzawi, D., Aldhaheri, S., & Pitropakis, N. (2023). BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things. Computer Systems Science and Engineering, 46(3), 3667-3684. https://doi.org/10.32604/csse.2023.037531

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple s... Read More about BIoMT: A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things.

A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy (2023)
Journal Article
Momand, A., Jan, S. U., & Ramzan, N. (2023). A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy. Journal of Sensors, 2023, Article 6048087. https://doi.org/10.1155/2023/6048087

Recently, intrusion detection systems (IDS) have become an essential part of most organisations’ security architecture due to the rise in frequency and severity of network attacks. To identify a security breach, the target machine or network must be... Read More about A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy.

All Outputs (45)