Husnain Rafiq
Husnain Rafiq
Lecturer in Cyber Security, Edge Hill University, Ormskirk, UK
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Security hardening of botnet detectors using generative adversarial networks
RH Randhawa, N Aslam, M Alauthman, H Rafiq, F Comeau
IEEE Access 9, 78276-78292, 2021
Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things
H Rafiq, N Aslam, U Ahmed, JCW Lin
IEEE Transactions on Industrial Informatics 19 (1), 960-968, 2022
Android malware classification using machine learning and bio-inspired optimisation algorithms
J Pye, B Issac, H Rafiq, N Aslam
19th IEEE International Conference on Trust, Security and Privacy in …, 2020
An investigation on fragility of machine learning classifiers in android malware detection
H Rafiq, N Aslam, B Issac, RH Randhawa
IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops …, 2022
Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection
RH Randhawa, N Aslam, M Alauthman, M Khalid, H Rafiq
Future Generation Computer Systems 150, 294-302, 2024
AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems
H Rafiq, N Aslam, M Aleem, B Issac, RH Randhawa
Scientific Reports 12 (1), 19534, 2022
Evasion generative adversarial network for low data regimes
RH Randhawa, N Aslam, M Alauthman, H Rafiq
IEEE Transactions on Artificial Intelligence, 2022
Key to Kindness: Reducing Toxicity In Online Discourse Through Proactive Content Moderation in a Mobile Keyboard
M Warner, A Strohmayer, M Higgs, H Rafiq, L Yang, L Coventry
arXiv preprint arXiv:2401.10627, 2024
AI-Based Hand Gesture Recognition Through Camera on Robot
G Csonka, M Khalid, H Rafiq, Y Ali
2023 International Conference on Frontiers of Information Technology (FIT …, 2023
On Impact of Adversarial Evasion Attacks on ML-based Android Malware Classifier Trained on Hybrid Features
H Rafiq, N Aslam, B Issac, RH Randhawa
2022 14th International Conference on Software, Knowledge, Information …, 2022
Android malware detection using machine learning to mitigate adversarial evasion attacks
H Rafiq
PQDT-Global, 2022
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