Welcom to Cybersecurity Innovation Center
About CIC
.aspx?width=auto&height=auto)
The Cybersecurity Innovation Center stands as a beacon of cybersecurity excellence, fostering a secure digital future for our community and beyond.
Together, we embrace innovation, education, and collaboration to build a resilient and safe cyber environment for all.
Cyber and AI Club
Comming Soon....
Cyber Hive Labritory
State-of-the-Art Laboratory
The CIC boasts a state-of-the-art laboratory, providing an immersive and hands-on experience in defending critical public infrastructure. Our students gain invaluable practical skills that prepare them to address real-world challenges in the field of cybersecurity.
.jpg?width=auto&height=auto)
Research
Advancing Research and Innovation: The CIC is at the forefront of research in cybersecurity, distributed computing, data science, artificial intelligence, machine learning, blockchain, cloud computing, fault tolerance, cryptography, and more. We encourage faculty and students to collaborate on cutting-edge projects that advance knowledge and best practices in cybersecurity.
Enabling Technology Research: Our laboratory is fully equipped to explore the application of machine learning to cybersecurity, with a particular focus on malware analysis. Additionally, we delve into the study of high-performance distributed systems and security to fortify digital infrastructure against cyber threats.
RESEARCH INTERESTS:
- Track 1: Cybersecurity of the critical infrastructure systems, protecting supervisory control and data acquisition (SCADA) networks, Cyberattack prevention, defending and mitigating.
- Track 2: High-performance computing, scheduling algorithms, cloud computing, big data analytics, AI, Machine learning, and robotics.
CURRENT ONGOING RESEARCH:
- Preventing algorithms and strategies for Phishing Attacks.
- Defending Cyber Attacks Targeting IoT in Critical infrastructure.
- Cybersecurity Attacks and Defenses Characteristics.
- Utlizing AI in Preventing Cyber attacks.
- DeepSched: A Novel Deep Learning-Based Scheduling Algorithm for Cloud Computing Systems: In this research, we try to propose a novel scheduling algorithm for cloud computing based on deep learning techniques that aim to minimize latency and maximize throughput. Our algorithm utilizes a deep neural network that adapts to the changing workload and resource availability in real-time, resulting in optimized resource allocation.
- Distinguishing Emotions in a voice using machine learning: Using AI to determine the emotional status of the speaker, focused on 6 types of emotions (happy, mad, sad, bored, surprised, or normal).
- SkinScan: AI-Powered Image Recognition for Dermatological Diagnosis: SkinScan is a novel system proposed in this research that utilizes AI image recognition to identify skin problems by training a convolutional neural network (CNN) with a comprehensive dataset of dermatological images.
- Advancing Autonomous Robotics Navigation: A Novel Deep Learning Algorithm for Increased Accuracy: This research introduces a groundbreaking deep learning algorithm aimed at revolutionizing autonomous robotics navigation. By combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the algorithm enables robots to learn and adapt their navigation strategies in real time.
PUBLICATIONS:
- Al-Sinayyid, A. MD, et al. (2023). Defending Characteristics and Attribution Analysis for Phishing Attacks (Manuscript submitted for publication in IEEE)
- Al-Sinayyid, Sas, A. et al. (2023). A Literature Survey and Analysis of Defending Cyber Attacks Targeting IoT in Critical infrastructure (Manuscript submitted for publication in IEEE)
- Al-Sinayyid, A. V, et al. (2023). Analytical Study for Cybersecurity Attacks and Defenses Characteristics (Manuscript submitted for publication in IEEE)
- Job scheduler for streaming applications in heterogeneous distributed processing systems. Al-Sinayyid, A., Zhu, M. Job scheduler for streaming applications in heterogeneous distributed processing systems. Springer, The Journal of Supercomputing 76, 9609–9628 (2020). https://doi.org/10.1007/s11227-020-03223-z
- A. Al-Sinayyid and M. Zhu. Maximizing the Processing Rate for Streaming Applications in Apache Storm. In Proceedings of the 14th International Conference on Data Science (ICDATA'18) pp. 143~146 Las Vegas, USA, July 30 - August 2, 2018.
- A. Abdul Hussien Abdul Wahed Alsinayyid, “Wireless Telecommunications uprising through 4G Long Term Evolution (LTE)”, Journal of Al-Qadisiyah for computer science and Mathematics, 3(1), pp. 221-230 (Published in 2017). Available at: http://qu.edu.iq/journalcm/index.php/journalcm/article/view/255
Education
CIC collaborates with faculty members to develop specialized coursework for students aiming to enter the dynamic cybersecurity workforce. Our courses are designed to meet the demands of industry and equip students with hands-on experience in defending critical public infrastructure like chemical plants, water, and power plants.
Community Services
Education and Community Outreach: The CIC takes a proactive approach to cybersecurity by working externally to educate the community about the ever-evolving threats. We conduct workshops, seminars, and awareness programs for students, government employees, and other community members, providing them with the latest techniques to protect personal data and businesses from cyber-attacks.
Profissional Advisory Comittee
Comming Soon....
Cyber Awareness and Training
Comming Soon....
Cyber Resources
Comming Soon....
Student Shoolerships and Internships
Comming Soon....