01280 828276 – ext: 3276
Hisham is a Reader in Computer Science. He is currently the Programme Director of MSc Innovative Computing and MSc Applied Computing in addition to being the Research Lead of the School of Computing.
Hisham leads the Cyber Security Stream, and he leads and teaches a number of UG and PG modules such as Fundamentals of Cyber Security and Cryptography, Ethical Hacking, Digital Forensics and Cyber Incident Management, and Information Security in Communications. He is also the module lead of Advanced Web Applications Development and Web Technologies and Applications.
Hisham’s key research interest is machine learning in both cyber security and healthcare. He has authored and co-authored 50+ publications in the two areas. Security applications include deep learning for biometric recognition, CCTV image analysis, identity-based encryption, machine learning for cyber security attacks and defences, digital forensics, privacy-aware and multi-factor authentication. He has successfully supervised tens of cyber security related projects ranging at UG, MSc, MSc by Research and PhD levels.
His research into healthcare focusses on the exciting field of automatic analysis of medical and biomedical images. He supervised/co-supervised PhD students working on collaborative projects such as X-ray and microscopic image analysis for identifying the genetic bases of complex diseases, brain image analysis for better understating of Alzheimer’s disease, inter\intra cellular calcium analysis for understanding atrial fibrillation, and ultrasound image analysis for identifying signs of early miscarriage and different types of ovarian tumours.
Hisham jointly won two Knowledge Transfer Partnerships (KTP) grants funded by Innovate UK & Deepnet Security LTD to build innovative biometric-based products and he was the academic supervisor of the two KTP projects.
He is an elected Senate Member and a member of the University Research committee. He is also a member of the British Computer Society and its Information Security Specialist Group.
Al-Maliki, O. and Al-Assam, H., 2021. Challenge-response mutual authentication protocol for EMV contactless cards. Computers & Security, 103, p.102186.
H. Al-Assam, W.K. Hassan and S. Zeadally, “Automated Biometric Authentication with Cloud Computing”, Biometric-Based Physical and Cybersecurity Systems, Springer, ISBN 978-3-319-98734-7, pp 455-475, 2019.
H. Al-Assam, T. Kuseler, S. Jassim and S. Zeadally, “Privacy in Biometric Systems”, in S. Zeadally & M. Badra (eds), Privacy in a Digital, Networked World: Technologies, Implications and Solutions, Springer, ISBN 978-3-31908469-5, pp. 235-263, October 2015
Al-Maliki, O. and Al-Assam, H., 2021. A tokenization technique for improving the security of EMV contactless cards. Information Security Journal: A Global Perspective, pp.1-16.
Al-Karawi, D., Ibrahim, D., Al-Assam, H., Du, H. and Jassim, S., 2021, April. A model-based adaptive method for speckle noise reduction in ultrasound images of ovarian tumours: a new approach. In Multimodal Image Exploitation and Learning 2021 (Vol. 11734, p. 117340J). International Society for Optics and Photonics.
Al-Karawi, D., Al-Assam, H., Du, H., Sayasneh, A., Landolfo, C., Timmerman, D., Bourne, T. and Jassim, S. An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses. Ultrasonic Imaging, 2021, p.0161734621998091.
Al-Showarah, S., Alzyadat, W., Alhroob, A. and Al-Assam, H., 2020. User Identification Based on the Dynamic Features Extracted from Handwriting on Touchscreen Devices. International Journal of Interactive Mobile Technologies, 14(11).
Al-Maliki, O. and Al-Assam, H., On the Security of the EMV Authentication Methods of Contactless Cards. In ECCWS 2020 20th European Conference on Cyber Warfare and Security, 2020, pp. 1-12.
Al-karawi, D., Landolfo, C., Du, H. Al-Assam, H., Sayasneh, A., Timmerman, D., Bourne, T. and Jassim, S.A., “A machine-learning algorithm to distinguish benign and malignant adnexal tumours from ultrasound images”, 4th International IOTA 2019 Congress, Berlin, Germany, 2019.
D. Al‐karawi, C. Landolfo, H Du, H. Al‐Assam, A Sayasneh, D Timmerman, T Bourne, S Jassim “Prospective clinical evaluation of texture‐based features analysis of ultrasound ovarian scans for distinguishing benign and malignant adnexal tumors”, Australasian Journal of Ultrasound in Medicine, 2019, Vol 22(2),pp.144-144.
Martínez-Más, J., Bueno-Crespo, A. Khazendar, S., Remezal-Solano, M., Martínez-Cendán, J.P., Jassim, S., Du, H. Al Assam, H., Bourne, T. and Timmerman, D. “Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images”, PloS one, 2019 14(7).
O. AlOkashi, H. Du, H. Al-Assam, “Automatic spine curvature estimation from X-ray images of a mouse model”, Journal of Computer Methods and Programs in Biomedicine, March 2017; Vol. 140, pp.175-184.
S. Pal, H. Al-Assam, H. Sellahewa, “On the discrimination power of dynamic features for online signature”, 22nd International Conference on Digital Signal Processing (DSP), London, 2017, pp. 1-5.
D. Traore, K. Rietdorf, N. Al-Jawad, H. Al-Assam, “Automatic Hotspots Detection for Intracellular Calcium Analysis in Fluorescence Microscopic Videos”, MIUA 2017: Medical Image Understanding and Analysis, July 2017, CCIS Series vol. 723, Springer, pp. 862-873.
S. Hussein, S. Jassim, H Al-Assam, “Automatic Quantification of Epidermis Curvature in H&E Stained Microscopic Skin Image of Mice”, MIUA 2017: Medical Image Understanding and Analysis, July 2017, CCIS Series vol. 723, Springer, pp. 935-945.
D. Ibrahim, H. Al-Assam, S. Jassim, H. Du, “Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images“, MIUA 2017: Medical Image Understanding and Analysis, July 2017, CCIS Series vol. 723, Springer, pp. 86-97.
W. Hassan, H. Al-Assam, “Key Exchange Using Biometric Identity Based Encryption for Sharing Encrypted Data in Cloud Environment”, in Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications, May 2017, pp. 102210J-102210J-7.O. Al-Okashi, H. Al-Assam, H. Du, “Automatic pelvis segmentation from x-ray images of a mouse model”, in Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications, May 2017, pp. 1022108-1022108-5.
T. Albaidhani, S. Jassim, H. Al-Assam, “Computer aided solution for segmenting the neuron line in hippocampal microscope images”, in Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications, May 2017, pp. 1022109-1022109-8.
D. Ibrahim, H. Al-Assam, H. Du, S. Jassim, “Trainable segmentation of multilocular cysts based on local basic pixel features”, in Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications, May 2017, pp. 102210B-102210B-8.
D. Al-karawi, A. Sayasneh, H. Al-Assam, S. Jassim, N. Page, D. Timmerman, T. Bourne, H. Du, “Automated differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images”, in Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications, May 2017, pp. 102210F-102210F-10.
S. Khazendar, J. Farren, H. Al-Assam, H. Du, A. Sayasneh, T. Bourne, S. Jassim, ‘Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac’, Annals of the BMVA Journal, 2015, Vol. 2015(5), pp. 1-6.
Khazendar, S., Sayasneh, A., Al-Assam, H., Du, H., Kaijser,J., Ferrara, L., Timmerman, D., Jassim, S., Bourne, T., “Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator”, Facts, Views and Visions in ObGyn 7 (1) , 2015, pp. 7-15<< Back to the directory