All Outputs (39)

Fault diagnosis based on extremely randomized trees in wireless sensor networks (2020)
Journal Article
Saeed, U., Jan, S. U., Lee, Y., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering and System Safety, 205, Article 107284. https://doi.org/10.1016/j.ress.2020.107284

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or fault... Read More about Fault diagnosis based on extremely randomized trees in wireless sensor networks.

A distributed sensor-fault detection and diagnosis framework using machine learning (2020)
Journal Article
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777-796. https://doi.org/10.1016/j.ins.2020.08.068

The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resour... Read More about A distributed sensor-fault detection and diagnosis framework using machine learning.

Toward a Lightweight Intrusion Detection System for the Internet of Things (2019)
Journal Article
Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a Lightweight Intrusion Detection System for the Internet of Things. IEEE Access, 7, 42450-42471. https://doi.org/10.1109/access.2019.2907965

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called... Read More about Toward a Lightweight Intrusion Detection System for the Internet of Things.

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification (2018)
Journal Article
Jan, S. U., & Koo, I. (2018). A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors, 2018, Article 7467418. https://doi.org/10.1155/2018/7467418

The efficiency of a binary support vector machine- (SVM-) based classifier depends on the combination and the number of input features extracted from raw signals. Sometimes, a combination of individual good features does not perform well in discrimin... Read More about A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification.

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio (2018)
Journal Article
Jan, S. U., Vu, V., & Koo, I. (2018). Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio. Applied Sciences, 8(3), Article 421. https://doi.org/10.3390/app8030421

A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional... Read More about Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio.

Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features (2017)
Journal Article
Jan, S. U., Lee, Y., Shin, J., & Koo, I. (2017). Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features. IEEE Access, 5, 8682-8690. https://doi.org/10.1109/access.2017.2705644

This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data si... Read More about Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features.

Application of Navigating System based on Bluetooth Smart (2017)
Journal Article
Lee, Y., Jan, S. U., & Koo, I. (2017). Application of Navigating System based on Bluetooth Smart. Journal of the Institute of Internet, Broadcasting and Communication, 17(1), 69-76. https://doi.org/10.7236/jiibc.2017.17.1.69

Bluetooth Low Energy (BLE), also known as Bluetooth Smart, has ultra-low power consumption; in fact, BLE-enabled devices can run on a single coin cell battery for several years. In addition, BLE can estimate the approximate distance between two devi... Read More about Application of Navigating System based on Bluetooth Smart.

Modeling and Analysis of DIPPM: A New Modulation Scheme for Visible Light Communications (2015)
Journal Article
Jan, S. U., Lee, Y., & Koo, I. (2015). Modeling and Analysis of DIPPM: A New Modulation Scheme for Visible Light Communications. Journal of Sensors, 2015, Article 963296. https://doi.org/10.1155/2015/963296

Visible Light Communication (VLC) uses an Intensity-Modulation and Direct-Detection (IM/DD) scheme to transmit data. However, the light source used in VLC systems is continuously switched on and off quickly, resulting in flickering. In addition, rece... Read More about Modeling and Analysis of DIPPM: A New Modulation Scheme for Visible Light Communications.

A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things
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.

Transfer learning-based method for detection of COVID-19 using X-Ray Images
Presentation / Conference Contribution
Rehman, A., Tariq, Z., Jan, S. U., Aziz, S., Khan, M. U., & Chaudry, H. N. (2021, October). Transfer learning-based method for detection of COVID-19 using X-Ray Images. Presented at 2021 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan

In this paper, we have performed transfer learning using different pre-trained convolutional neural networks for binary classification of X-ray images into COVID-19 disease and normal. The dataset is gathered from two open sources. Our dataset is con... Read More about Transfer learning-based method for detection of COVID-19 using X-Ray Images.

Comparative analysis of DIPPM scheme for Visible Light Communications
Presentation / Conference Contribution
Jan, S. U., Lee, Y.-D., & Koo, I. (2015, December). Comparative analysis of DIPPM scheme for Visible Light Communications. Presented at 2015 International Conference on Emerging Technologies (ICET), Peshawar, Pakistan

Visible Light Communications (VLC) uses solid-state light sources for data transmission in addition to its primary function of illumination. The dual functionality of light source provokes some challenges for VLC including dimming control and perceiv... Read More about Comparative analysis of DIPPM scheme for Visible Light Communications.

Sensor faults detection and classification using SVM with diverse features
Presentation / Conference Contribution
Jan, S. U., & Koo, I. S. (2017, October). Sensor faults detection and classification using SVM with diverse features. Presented at 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea

Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these... Read More about Sensor faults detection and classification using SVM with diverse features.

Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks
Presentation / Conference Contribution
Jan, S. U., Vu, V. H., & Koo, I. S. (2018, October). Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks. Presented at 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, China

In this work, the performance of support vector machine (SVM)-based classifier, applied for spectrum sensing in cognitive radio (CR) networks, is analyzed. A single observation given input to classifier is composed of three statistical features extra... Read More about Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks.

On Lightweight Method for Intrusions Detection in the Internet of Things
Presentation / Conference Contribution
Shakhov, V., Jan, S. U., Ahmed, S., & Koo, I. (2019, June). On Lightweight Method for Intrusions Detection in the Internet of Things. Presented at 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, Russia

Integration of the internet into the entities of the different domains of human society is emerging as a new paradigm called the Internet of Things. At the same time, the ubiquitous and wide-range systems make them prone to attacks. Security experts... Read More about On Lightweight Method for Intrusions Detection in the Internet of Things.

Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems
Presentation / Conference Contribution
Jan, S. U., Saeed, U., & Koo, I. (2020, January). Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems. Presented at 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan

Cyber-Physical System (CPS) emerges as a potential direction to improve the applications relating to object-to-object, human-to-human and human-to-object communications in both the real world and virtual world. The examples of CPSs include Smart Gird... Read More about Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems.

Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi
Presentation / Conference Contribution
Saeed, U., Ullah Jan, S., Lee, Y.-D., & Koo, I. (2020, January). Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi. Presented at 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain

From smart industries to smart cities, sensors in the modern world plays an important role by covering a large number of applications. However, sensors get faulty sometimes leading to serious outcomes in terms of safety, economic cost and reliability... Read More about Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi.

Automated Grading of Diabetic Macular Edema Using Color Retinal Photographs
Presentation / Conference Contribution
Zubair, M., Ahmad, J., Alqahtani, F., Khan, F., Shah, S. A., Abbasi, Q. H., & Jan, S. U. (2022, May). Automated Grading of Diabetic Macular Edema Using Color Retinal Photographs. Presented at 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia

Diabetic Macular Edema (DME) is an advanced indication of diabetic retinopathy (DR). It starts with blurring in vision and can lead to partial or even complete irreversible visual compromise. The only cure is timely diagnosis, prevention and treatmen... Read More about Automated Grading of Diabetic Macular Edema Using Color Retinal Photographs.

A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data
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.

ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT
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.