Saleh Alabdulwahab

• Master of Engineering
• PhD Student at Dongguk University

The growing integration of cloud and embedded systems heightens the importance of security. Although various defenses have been introduced, evolving threats persist—especially deep learning (DL)-based side-channel disassembly attacks that reveal cloud-to-device operations. To address this, we developed a robust, instruction-level side-channel disas...

The increased usage of IoT networks brings about new privacy risks, especially when intrusion detection systems (IDSs) rely on large datasets for machine learning (ML) tasks and depend on third parties for storing and training the ML-based IDS. This study proposes a privacy-preserving synthetic data generation method using a conditional tabular gen...

Networks within the Internet of Things (IoT) have some of the most targeted devices due to their lightweight design and the sensitive data exchanged through smart city networks. One way to protect a system from an attack is to use machine learning (ML)-based intrusion detection systems (IDSs), significantly improving classification tasks. Training...

The detection accuracy and model building time of machine learning (ML) classifiers are vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently, researchers have introduced feature selection methods to increase the detection accuracy and minimize the model building time of a limited number of ML classifiers....