Shahrzad has worked in the computing industry for over 15 years and gained a great deal of experience in computer hardware/software and business management. She has developed a passion for digital forensics and security, where she can employ her background knowledge in applied statistics and data mining in order to improve network security. At the moment, Shahrzad is the course leader in Computer Security with Forensics and is involved with teaching different topics in digital forensics.
Shahrzad holds a PhD in Applied Statistics from the University of Leeds, MSc in Forensics Computing and Security, and BSc Hons in Statistics. In addition, she holds a PgCert in Learning and Teaching in Higher Education and became a Fellow in HEA in January 2015. She has worked in the IT industry for more than 15 years and started teaching in Higher Education from 2012. Her professional skills consist of application of statistical analysis, mathematical modelling, data mining, enterprise management, disaster recovery, business continuity and contingency, network security, digital forensics (including mobile forensics), cyber security, expert witness, security technologies, incident response, intrusion detection and ethical hacking.
She is an active researcher with many publications in different conferences and journals as well as being a member of different conferences’ program committees.
Specialist areas of interest
Business Disaster Recovery
Business Continuity and Contingency
Department of Computing
Business, Technology and Enterprise
Computer Systems and Networks
BSc/MComp Computer Security with Forensics
Computer Forensics Investigation and Response (L7)
Computer Forensics Expert Witness (L6) Digital Forensics (L5)
Network Intrusion Detection (L5) Network Services and Administration (L5)
Computer Systems and Architecture(L4) Introduction to Computer and Information Security (L4)
Anomaly detection using data mining, mobile forensics investigations, Cybersecuirty, Enhancing Student learning experience in HE, Computer laws, Pattern matching in Phishing attacks, Cyber Criminal profiling (OSINT).
Mwitondi, K., Said, R.A., & Zargari, S. (2019). A robust domain partitioning intrusion detection method. Journal of information security and applications, 48. http://doi.org/10.1016/j.jisa.2019.102360
Ajao, S., Bhowmik, D., & Zargari, S. (2019). Sentiment aware fake news detection on online social networks. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). http://doi.org/10.1109/ICASSP.2019.8683170
Mwitondi, K., & Zargari, S. (2018). An iterative multiple sampling method for intrusion detection. Information Security Journal: A Global Perspective, 27 (4), 230-239. http://doi.org/10.1080/19393555.2018.1539790
Mwitondi, K.S., Al-Kuwari, F.A., Saeed, R.A., & Zargari, S. (2018). A statistical downscaling framework for environmental mapping. Journal of Supercomputing. http://doi.org/10.1007/s11227-018-2624-y
Ajao, O., Bhowmik, D., & Zargari, S. (2018). Fake news identification on Twitter with hybrid CNN and RNN models. 9th International Conference on Social Media & Society, 226-230. http://doi.org/10.1145/3217804.3217917
Pournouri, S., Zargari, S., & Akhgar, B. (2019). An Investigation of Using Classification Techniques in Prediction of Type of Targets in Cyber Attacks. Proceedings of 12th International Conference on Global Security, Safety and Sustainability, ICGS3 2019. http://doi.org/10.1109/ICGS3.2019.8688266
Ajao, O., Bhowmik, D., & Zargari, S. (2018). Content-aware tweet location inference using quadtree spatial partitioning and jaccard-cosine word embedding. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, 1116-1123. http://doi.org/10.1109/ASONAM.2018.8508257
Mwitondi, K., Said, R., & Zargari, S. (2017). An Ensemble Method for Intrusion Detection with Conformity to Data Variability. The Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2017). http://www.bigdataclouds.org/
Janarthanan, T., & Zargari, S. (2017). Feature Selection in UNSW-NB15 and KDDCUP’99 datasets. ISIE 2017. http://doi.org/10.1109/ISIE.2017.8001537
Mwitondi, K., & Zargari, S. (2017). A Repeated Sampling and Clustering Method for Intrusion Detection. International Conference in Data Mining (DMIN'17), 91-96. http://csce.ucmss.com/cr/books/2017/LFS/CSREA2017/DMI3482.pdf
Abdullahi Yari, I., & Zargari, S. (2017). An overview and computer forensic challenges in image steganography. In Proceedings, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), (pp. 360-364). IEEE: http://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.60
Palmieri, G., & Zargari, S. (2017). Using open source forensic carving tools on split dd and EWF files. In Proceedings, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), (pp. 379-383). IEEE: http://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.183
Zargari, S. (2017). Feature Selection in the Corrected KDD -dataset. In International Conference on Big Data in Cyber Security 2017, Cyber Academy, Edinburgh, 10 May 2017. http://thecyberacademy.org/bigcyber2017/
Zargari, S., & Janarthanan, T. (2015). The evidentiary value of link files in Linux file system to digital forensic investigation. International Conference on Computer and Information Technology, 1984-1988. http://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.294
Janarthanan, T., & Zargari, S. (2015). The Evidentiary Value of Link Files in Linux File System to Digital Forensic Investigation. CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING, 1985-1989. http://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.294
Zargari, S., & Smith, A. (2013). Policing as a service in the cloud. In 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies, Xi'an, (pp. 589-596). IEEE: http://doi.org/10.1109/EIDWT.2013.106
Zargari, S., & Benford, D. (2012). Cloud forensics : concepts, issues, and challenges. In EIDWT 2012 Third International Conference on Emerging Intelligent Data and Web Technologies, (pp. 236-243). IEEE
Ajao, O., Bhowmik, D., & Zargari, S. (2018). Fake News Identification on Twitter with Hybrid CNN and RNN Models. 9th Int'l Conference on Social Media & Society. Copenhagen (July 18).
Pournouri, S., Zargari, S., & Akhgar, B. (2018). Predicting the cyber attackers; A comparison of different classification techniques. In Advanced Sciences and Technologies for Security Applications. (pp. 169-181). http://doi.org/10.1007/978-3-319-97181-0_8
Theses / Dissertations
Ajao, O. (2019). Content-aware Location Inference and Misinformation in Online Social Networks. (Doctoral thesis). Supervised by Zargari, S. http://doi.org/10.7190/shu-thesis-00252
Zargari, S. (2016). Computer Forensics Community : A Case Study. Presented at: 12th Annual Teaching Computer Forensics Workshop, Sunderland, UK, 2016
Zargari, S., & Bagheri Zadeh, P. (2015). Enhancing student engagement in Digital Forensics. Presented at: 11th Annual Teaching Computer Forensics Workshop, University of Sunderland
De Montfort University