Coordinated by Prof. Dr. Hasan Dağ, Director at Kadir Has University's Center for Cybersecurity & Critical Infrastructure Protection (KHAS_CCIP), the research project, code name 'WARNING,' designs and offers a holistic cybersecurity platform that is capable of detecting artificial intelligence-based cyber threats and producing analysis results thereon employing the state-of-the-art approaches.
Cybersecurity is becoming more and more important in our daily lives. Nevertheless, vulnerabilities at an "interdependency" level are concurrently increasing, which could make any structure and system further open to and exploitable by cyber-attacks.
Aiming to bring non-conventional and big data, artificial intelligence-based, innovative approaches to the field of cybersecurity research and applications, Kadir Has University's Center for Cybersecurity & Critical Infrastructure Protection (KHAS_CCIP) places a premium on the training of labor force, which is "the weakest link" of cybersecurity. Conducting interdisciplinary studies, the KHAS_CCIP team carries out an important research project that Prof. Dr. Hasan Dağ* coordinates.
Entitled to support under the scope of a Bilateral Cooperation Project between TUBITAK and QNRF (Qatar National Research Fund), the project called "A Defense-in-depth Cyber Intelligence Platform to Defend Against Emerging Cyber-Attacks" (code name: WARNING) was carried out between September 15, 2018, and May 4, 2021. Kadir Has University and TOBB University of Economics and Technology have jointly applied to the call for the project under the consortium UITSEC International.
The project was participated by Kadir Has University, Intelprobe (replacing UITSEC), TOBB University of Economics and Technology, and the Qatar Computing Research Institute under the roof of Hamad Bin Khalifa University, as well as the Qatar Ministry of Interior Supreme Committee for Delivery and Legacy. In this project, Kadir Has University was represented by nine people: one project coordinator (Prof. Dr. Hasan Dağ), two PhD students, two graduate students, and four undergraduate students.
The project's primary purpose was to design and offer a holistic cybersecurity platform capable of detecting artificial intelligence-based cyber threats and producing analysis results thereon employing state-of-the-art approaches. The KHAS_CCIP Team was responsible for two work packages: Our researchers worked in 3 groups: Group I was responsible for developing a static malware detection model, Group II for developing a dynamic and hybrid malware detection model, and Group III for designing user interfaces and offering models as a service.
Having successfully completed these responsibilities at the end of the research period, the teams also released two international conference statements, published an article at a Q1 category magazine, and completed 1 PhD thesis. Here is the list of the related publications:
1. Çayır A., Ünal U., Yenidoğan, I., & Dağ, H. (2019). Use Case Study: Data Science Application for Microsoft Malware Prediction Competition on Kaggle. Proceedings Book, 98.
2. Demirkıran, F., Çayır, A., Ünal, U., & Dağ, H. (2020, September). Website category classification using fine-tuned BERT language model. In 2020 5th International Conference on Computer Science and Engineering (UBMK) (pp. 333-336). IEEE.
3. Çayır, A., Ünal, U., & Dağ, H. (2021). Random CapsNet forest model for imbalanced malware type classification task. Computers & Security, 102, 102133.
(*) R&D Resources Director and Vice President (Research)