AI-Based Cryptanalysis of Lightweight Cryptographic Algorithms
Supervisor Name
Abdallah Rashed
Supervisor Email
a.rashed@najah.edu
University
An-Najah National University
Research field
Computer Science
Bio
Abdallah Rashed earned a BSc in Computer Engineering from An-Najah National University (2008). He completed his MSc and PhD in Computer Science and Engineering at King Fahd University of Petroleum and Minerals (KFUPM) in 2018, specializing in wireless sensor network security. He has since been an Assistant Professor at An-Najah (since August 2018), with prior experience as a lecturer, instructor, and IT specialist at both institutions, including work on E-learning quality.
Description
This project investigates the application of artificial intelligence and deep learning techniques to automate the security evaluation of lightweight cryptographic algorithms tailored for IoT and embedded systems. Focusing on widely adopted ciphers—namely PRESENT, SIMON, and SPECK—the study explores how intelligent models can effectively identify subtle structural weaknesses and cryptographic biases. The research workflow involves generating large-scale datasets of encryption–decryption instances to train specialized neural network architectures capable of recognizing vulnerability patterns. The performance of the developed AI models will be systematically assessed in terms of detection accuracy, execution efficiency, and practical applicability, alongside a comparative analysis against conventional cryptanalysis methods. The overarching goal is to establish an effective, AI-powered cryptanalysis toolset that advances the vulnerability assessment of lightweight ciphers.