PhD Candidates
Syed Rizvi
Forensics and Security Research Group
Syed Rizvi is a PhD candidate in the Forensics and Security Research Group. A fuller biography, research profile, and headshot will be added soon.
Research Output
Publications
An AI-Based Network Forensic Readiness Framework for Resource-Constrained Environments
Proceedings of the 18th International Workshop on Digital Forensics, part of the 20th International Conference on Availability, Reliability and Security
This paper presents an AI-based network forensic readiness framework for resource-constrained environments. The framework integrates optimised artificial intelligence models to detect attacks in real-time, capturing and preserving critical forensic artefacts. It aligns with ISO/IEC 27043:2015 Digital Forensic Readiness principles, reducing time and human effort.
Pushing Network Forensic Readiness to the Edge: A Resource Constrained Artificial Intelligence Based Methodology
2024 Cyber Research Conference - Ireland (Cyber-RCI)
This paper introduces the Network Forensic Readiness for Edge Devices (NetFoREdge) framework, which deploys lightweight AI models in resource-constrained environments for attack detection, evidence collection, and preservation. The framework is evaluated on two datasets, achieving accuracy rates exceeding 99.60% and 99.98% for multiclassification.
An Evaluation of AI-Based Network Intrusion Detection in Resource-Constrained Environments
14th Annual IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON)
This paper evaluates AI-based network intrusion detection in resource-constrained environments, proposing a novel approach that trains and deploys AI models on resource-constrained devices. The approach achieves high classification accuracy, identifying and recording potential malicious attacks in real-time with minimal overhead.
Deep Learning Based Network Intrusion Detection System for Resource-Constrained Environments
The 13th EAI International Conference on Digital Forensics and Cyber Crime
This paper presents a deep learning-based network intrusion detection system (IDS) for resource-constrained environments. The proposed 1D-Dilated Causal Neural Network (1D-DCNN) model achieves high accuracy in detecting malicious attacks, outperforming existing deep learning approaches. The model's efficiency and effectiveness make it suitable for resource-constrained environments.
Application of Artificial Intelligence to Network Forensics: Survey, Challenges and Future Directions
IEEE Access Vol. 10
This paper provides a comprehensive survey of the application of artificial intelligence (AI) in network forensics, including expert systems, machine learning, deep learning, and ensemble/hybrid approaches. It discusses the current challenges and future directions in network forensics, covering various application areas such as network traffic analysis, intrusion detection systems, and Internet-of-Things devices.