Farah Jemili
Biography
Farah JEMILI has completed her Ph.D. in 2010, from the National School of Computer Sciences (ENSI), Tunisia. Since 2010, she is an Assistant Professor at the Higher Institute of Computer Science and Telecom of Hammam Sousse (ISITCOM), Tunisia. She has been member of the Scientific Council of ISITCOM for 3 years (2011-2014), and Head of the Department of Computer Science at ISITCOM for 3 years (2017-2020). Her research interests include Artificial Intelligence, Cyber Security and Big Data Analysis. She served as a Reviewer for many international conferences and journals. She has published around 45 Research papers in international journals and conferences and has presented many invited and contributed Talks at international conferences.
Research Interest
Generative Artificial Intelligence for Cyber Security
Abstract
In the face of escalating cyber threats, the fusion of artificial intelligence (AI) with cybersecurity strategies has become a cornerstone for modern defense systems. This presentation, delivered by Dr. Farah Jemili, explores the pivotal role of generative AI in fortifying cybersecurity frameworks. As modern communication technologies generate vast amounts of data daily, the presentation addresses the pressing need to harness this data to safeguard Industry 4.0 infrastructures, which are increasingly vulnerable to cyber-attacks. With cyber-attack damages projected to reach $8 trillion in 2024 and escalate to $10.5 trillion by 2025, innovative solutions are essential.
The presentation outlines the current cybersecurity landscape, identifying key challenges such as data collection, storage, processing, and the accurate detection of cyber threats. It emphasizes the transformative potential of AI methodologies, including machine learning, deep learning, and generative learning, in developing robust intrusion detection systems and real-time threat response mechanisms.
A detailed examination of real-world AI applications in cybersecurity is provided, showcasing technologies like Cylance for malware prevention, AEG for automatic exploit generation, AI2 for predictive threat analysis, and IBM's Watson for IoT network analytics. The comparative study of various deep learning models highlights their distinct advantages and limitations in cybersecurity contexts.
Furthermore, the presentation discusses the integration of AI with other technological pillars such as cloud computing and big data, illustrating how these synergies enhance cybersecurity capabilities. The research contributes to understanding how AI can be leveraged to develop intelligent, adaptive, and resilient cybersecurity systems, ultimately aiming to mitigate the risks and impacts of cyber-attacks.
The presentation concludes with perspectives on future research directions and the evolving landscape of AI in cybersecurity, underscoring the critical role of continuous innovation and collaboration in this domain.
