Mrs. Sarra Senouci the research work centers on advanced cryptographic systems, network security, and intelligent detection frameworks, with a strong emphasis on chaotic dynamics, pseudo-random number generation, and secure data transmission. The studies include the development of a novel pseudo-random number generator (PRNG) for fiber optic communication, leveraging nonlinear chaotic behavior to enhance cryptographic strength and improve resistance to prediction attacks. Additional contributions explore a chaotic-based cryptographically secure PRNG designed for high-performance applications requiring strong randomness and low computational overhead. In the domain of cybersecurity, the research introduces deep convolutional neural network architectures for high-precision and real-time DDoS attack detection within software-defined networking environments. This includes models optimized for both feature extraction and rapid classification to mitigate large-scale network threats. Further advancements incorporate feature engineering and ensemble learning techniques to achieve robust, scalable, and resilient DDoS detection frameworks capable of adapting to evolving attack patterns. Earlier academic work includes the design and construction of autonomous sensor networks and the implementation of chaotic systems on FPGA platforms, highlighting strong integration of hardware, communication technologies, and nonlinear system modeling across multiple layers of modern electronic and communication systems.