LAB-MA

Department of Computer Engineering · Faculty Research Lab

Prof. Musheer Ahmad

Professor / Associate Professor

Designing provably-secure S-Box constructions from nonlinear dynamical systems, extending chaos-based image encryption to multimedia workflows, and applying deep learning to authentication protocol vulnerability analysis.

Chaos CryptographyS-Box DesignDeep Learning SecurityAuth Protocols

Collaborate with this Lab →Lab Publications (7) ↓

Research Specialisations

Areas of active investigation.

01Chaos-based Cryptography & Image/Multimedia Encryption

02Cybersecurity & Authentication Protocols

03Machine Learning, Deep Learning & Soft Computing for Security

04Nonlinear Dynamics & Substitution-Box (S-Box) Design

Principal Investigator · Profile

Prof. Musheer Ahmad

Professor / Associate Professor

Department of Computer Engineering

Specialist in chaos-based cryptographic systems, substitution-box design, and machine learning applied to cybersecurity.

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Laboratory Literature

Published papers, preprints & inventions.

[LAB-MA]Chaos-based Cryptography & S-Box Design2018-10-05[PEER-REVIEWED]Citations: 142+

Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications

Ahmad, M., Doja, M. N., Beg, M. M. S.

IEEE Access

This paper employs Particle Swarm Optimization (PSO) combined with chaotic dynamical maps to generate highly nonlinear Substitution-Boxes (S-Boxes) for use in block cipher security applications. The generated S-Boxes exhibit superior nonlinearity, strict avalanche criterion, and resistance to differential and linear cryptanalysis attacks.

#Particle Swarm Optimization#S-Box#Nonlinear Dynamics#Block Cipher#Cryptography

DOI: 10.1109/ACCESS.2018.2871443 ↗View in Main Bibliography →

[LAB-MA]Chaos-based Cryptography & S-Box Design2021-03-24[PEER-REVIEWED]Citations: 85+

A Novel Method of S-Box Design Based on Discrete Chaotic Maps and Cuckoo Search Algorithm

Alhadawi, H. S., Majid, M. A., Lambić, D., Ahmad, M.

Multimedia Tools and Applications (Springer)

Proposes an algebraic method for synthesizing high-nonlinearity Substitution-Boxes using multidimensional discrete chaotic attractors coupled with Cuckoo Search meta-heuristics. The resulting S-Boxes yield superior resistance against differential and linear cryptanalysis, with performance validated against standard NIST criteria.

#Chaotic Maps#S-Box Design#Nonlinear Dynamics#Differential Cryptanalysis#Cuckoo Search

DOI: 10.1007/s11042-020-10444-2 ↗View in Main Bibliography →

[LAB-MA]Cybersecurity & Medical Image Encryption2023-05-18[PEER-REVIEWED]Citations: 47+

Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine

Ahmad, M., Khurram Khan, M., Tanveer, M.

IEEE Access

This work presents a fuzzy logic-augmented S-Box construction grounded in hyperchaotic dynamical systems for robust encryption of medical images in telemedicine environments. The scheme addresses the critical need for securing sensitive patient data during transmission, with validations on DICOM medical image benchmarks.

#Medical Image Encryption#Fuzzy Logic#S-Box#Telemedicine Security#Hyperchaos

DOI: 10.1109/ACCESS.2023.3270000 ↗View in Main Bibliography →

[LAB-MA]Nonlinear Dynamics & Multimedia Security2024-03-01[PEER-REVIEWED]Citations: 29+

Shuffle Medical Image Encryption Scheme Based on 4D Memristive Hyperchaotic Map

Ahmad, M., Alam, B., Umayya, Z., Khan, S., Khurram Khan, M.

Expert Systems with Applications (Elsevier)

Introduces a multi-dimensional memristive hyperchaotic map for constructing a shuffle-based image encryption scheme. The 4D system exhibits extreme sensitivity to initial conditions and large key space, ensuring strong statistical security properties validated through NIST, entropy, and correlation analysis.

#Memristive Hyperchaos#Image Encryption#4D Map#Medical Imaging Security#Nonlinear Dynamics

View in Main Bibliography →

[LAB-MA]Machine Learning & Intelligent Transportation2021-07-10[PEER-REVIEWED]Citations: 93+

Intelligent Driver Drowsiness Detection for Traffic Safety Based on Multi CNN Deep Model and Facial Subsampling

Ahmad, M., Al-Rakhami, M. S., Al-Amri, A., Ghoneim, A., Alrashoud, M., Khurram Khan, M.

IEEE Transactions on Intelligent Transportation Systems

A multi-branch CNN architecture combining facial landmark subsampling and eye-state classification for real-time driver drowsiness detection in vehicular safety systems. The model is validated on public drowsiness datasets and demonstrates high accuracy under variable lighting and occlusion conditions.

#Driver Safety#CNN#Drowsiness Detection#Intelligent Transportation#Facial Analysis

DOI: 10.1109/TITS.2021.3085013 ↗View in Main Bibliography →

[LAB-MA]Machine Learning & Computer Vision2021-02-14[PEER-REVIEWED]Citations: 71+

Detecting Distraction of Drivers Using Convolutional Neural Network

Ahmad, M., Khurram Khan, M., Alrashoud, M.

Pattern Recognition Letters (Elsevier)

Presents a convolutional neural network framework for detecting driver distraction behavior from in-cabin camera streams. The system classifies driver activities into distracted and focused categories, providing a deployable pipeline for automotive safety monitoring applications.

#Driver Distraction#CNN#Pattern Recognition#Automotive Safety#Computer Vision

View in Main Bibliography →

[LAB-MA]Cybersecurity & Authentication Protocols2024-01-20[PEER-REVIEWED]Citations: 18+

Cost-Effective PUF and ECC-Based Authentication Protocol for Secure Internet of Drones Communication

Rustam, F., Ahmad, M., de Rivera, D. S., Mehmood, A., Khurram Khan, M., Ashraf, I.

IEEE Internet of Things Journal

Proposes a lightweight authentication protocol for Internet of Drones (IoD) networks combining Physically Unclonable Functions (PUF) with Elliptic Curve Cryptography (ECC). The protocol resists replay, man-in-the-middle, and impersonation attacks while remaining computationally feasible for resource-constrained drone hardware.

#PUF#ECC#IoT Security#Internet of Drones#Authentication Protocol

DOI: 10.1109/JIOT.2024.3353012 ↗View in Main Bibliography →

Laboratory Mission Statement

Cryptographic Systems & Secure Intelligence Lab

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Prof. Musheer Ahmad

Professor / Associate Professor

Chaos CryptographyS-Box DesignDeep Learning SecurityAuth Protocols