ICONET’s research activities cover various topics, including cybersecurity, image processing on-board spacecraft, and Support Vector Machines (SVM) on-board small satellites.

We aim to promote excellence in education and innovation by developing and applying cutting-edge ICT solutions to real-world problems. Our team of experts is dedicated to advancing the field of ICT through research, development, and training programs.

At ICONET, we continue to push the boundaries of ICT research and innovation to address real-world challenges and contribute to the advancement of society.


Our cybersecurity research focuses on understanding and addressing organizations’ vulnerabilities due to the human factor. In a recent study conducted at a small-sized university, we analyzed the password generation patterns used by faculty, staff, and students. The findings revealed that users often need to be made aware of security requirements and practices. Alarmingly, most users' passwords were found to be breakable within a few days or even less. Interestingly, while numbers and uppercase letters were commonly used, they were often positioned predictably, with numbers frequently appearing at the end of passwords and uppercase letters predominantly at the beginning.

These patterns make it easier for attackers to develop more effective dictionaries. Our project aims to analyze password strength and provide recommendations to IT personnel and the general public on how to enhance the security of their passwords.

On-board Image Processing for Spacecraft Autonomy

The development of future spacecraft is intricately linked to the advancement of onboard autonomy, with image processing playing a pivotal role in realizing this vision. However, the computational limitations inherent to spacecraft pose significant challenges when implementing sophisticated image-processing algorithms. Satellite image classification plays a pivotal role in augmenting spacecraft autonomy, facilitating capabilities such as identifying priority regions for transmission and adjusting mission schedules. Despite the presence of literature on onboard image processing, there remains a significant need for more specific references that comprehensively address the state-of-the-art advancements and future trends in the context of small satellite missions.

Applying Support Vector Machines in Small Satellites

Support Vector Machines (SVM) have gained widespread recognition across various fields since their introduction in 1995, primarily due to their remarkable classification accuracy surpassing alternative techniques. However, the adoption of SVM in space applications has been limited, primarily attributed to its relative novelty compared to well-established competing methods.

The primary objective of this project is to delve into the unexplored realm of SVM applications in space. Two specific applications are proposed to unlock the potential of SVM in this domain. Firstly, SVM in classification mode will enhance onboard intelligence, empowering spacecraft to identify high-priority image data, such as natural disasters, without relying on previous image history. This approach assesses the feasibility of leveraging SVM by utilizing input features for adequate classification.

The second proposed application revolves around utilizing Support Vector Regression (SVR) in the critical area of Fault Detection, Isolation, and Recovery (FDIR) onboard spacecraft. FDIR plays a vital role in ensuring spacecraft autonomy, and the performance of SVR will be compared to existing approaches like Built-In Testing (BIT) to enhance fault detection and recovery processes.

To overcome the computational demands of machine learning in space, this project will employ a hardware-based implementation of SVM utilizing Field-Programmable Gate Arrays (FPGAs). The computational requirements of the software implementation will be analyzed, and its performance will be juxtaposed with the hardware implementation, paving the way for optimized SVM utilization in space applications.

Selected Projects

Project Name Sponsor

An AI-powered Arabic reading proficiency test 

( Sponsor: Mohammed Bin Rashid Smart Learning Program) 

Data Governance of UAE Learning Materials Big Data

( Sponsor: Mohammed Bin Rashid Smart Learning Program) 

AI-Based Learning Outcomes Assessment 

( Sponsor: Mohammed Bin Rashid Smart Learning Program) 

MeznSat: Methane Detecting CubeSat 

(Sponsor: UAE Space Agency) 

The House Guardian 

(Sponsor: Expo Live Funding) 

Adaptive Street Light LED Dimming with Diagnostics and Vehicle Tracking Capability for Energy Saving

(Sponsor: ICT Fund) 

Towards an E-health Microsystem: A Polarization-based camera for Biomedical and Health Applications

(Sponsor: ICT Fund)


Last Updated: 23 Nov 2023
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