Ayman Noor^*

Computer Systems Science and Engineering, Vol.49, pp. 259-286, 2025, DOI:10.32604/csse.2025.060387 - 21 February 2025

Abstract Precisely forecasting the performance of Deep Learning (DL) models, particularly in critical areas such as Uniform Resource Locator (URL)-based threat detection, aids in improving systems developed for difficult tasks. In cybersecurity, recognizing harmful URLs is vital to lowering risks associated with phishing, malware, and other online-based attacks. Since it directly affects the model’s capacity to differentiate between benign and harmful URLs, finding the optimum mix of hyperparameters in DL models is a significant difficulty. Two commonly used architectures for sequential and spatial data processing, Long Short-Term Memory (LSTM)/Gated Recurrent Unit (GRU) and Convolutional Neural Network… More >

Mohammad Adnan Aladaileh, Mohammed Anbar^*, Iznan H. Hasbullah, Abdullah Ahmed Bahashwan, Shadi Al-Sarawn

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1403-1416, 2022, DOI:10.32604/cmc.2022.029369 - 18 May 2022

Abstract The emergence of a new network architecture, known as Software Defined Networking (SDN), in the last two decades has overcome some drawbacks of traditional networks in terms of performance, scalability, reliability, security, and network management. However, the SDN is vulnerable to security threats that target its controller, such as low-rate Distributed Denial of Service (DDoS) attacks, The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component. Therefore, there is an urgent need to propose a detection approach… More >