Top of page

Student/Staff Portal
Global Site Navigation

School of Science

Local Section Navigation
You are here: Main Content

  • Dr Iqbal H Sarker

Related Contents

  • See Also
  • LinkedIn profile
    https://www.linkedin.com/in/iqbal-sarker-cse/
  • LinkedIn profile
  • ResearchGate profile
    https://www.researchgate.net/profile/Iqbal-Sarker
  • ResearchGate profile
  • Google Scholar profile
    https://scholar.google.com.au/citations?user=FR9o1hcAAAAJ&hl=en
  • Google Scholar profile
  • DBLP profile
    https://dblp.uni-trier.de/pid/132/9105.html
  • DBLP profile

Dr Iqbal H Sarker

Post Doctoral Research Fellow

Staff Member Details
Email: m.sarker@ecu.edu.au
Campus: Joondalup  
ORCID iD: https://orcid.org/0000-0003-1740-5517

Iqbal is a researcher (Research Focused Scholar - Academic Staff) focusing on Cybersecurity and AI/Data-driven technologies at the Centre for Securing Digital Futures, ECU.

Background

Dr. Iqbal H. Sarker received his PhD in Computer Science from Swinburne University of Technology, Melbourne, Australia, in 2018. He is currently a Research Fellow at the Centre for Securing Digital Futures, Edith Cowan University (ECU), Australia. He has also worked with the Cyber Security Cooperative Research Centre (CSCRC) through academia–industry collaborations, including with CSIRO’s Data61. In addition, Dr. Sarker is an Adjunct Fellow at the DigiSAS Lab, School of Computer Science, University of Technology Sydney (UTS), Australia.

Dr. Sarker’s research focuses on Cybersecurity, AI & Data-driven technologies, Digital twins and AI applications for Business Transformation and SMEs. He has published 100+ Journal and Conference papers in various reputed venues published by Elsevier, Springer Nature, IEEE, ACM, Oxford University Press, etc. Moreover, he is a LEAD author of two research monograph BOOKs titled "Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making”, Springer Nature, Switzerland, and “AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability”, Springer Nature, Switzerland. His research impact has been recognized through ranking by ScholarGPS and inclusion in the world’s top 2% of most-cited scientists, as published by Elsevier and Stanford University.

In addition to his research activities, Dr. Sarker has extensive teaching experience in courses such as AI, Machine Learning, Data Science and Analytics, Database Management Systems, Project Management and Programming. He is actively involved in research leadership and engagement roles, including journal editorial boards, international conference program committees, postgraduate student supervision, visiting scholar appointments, and academia-industry collaborations. He is also engaged with the Industry Mentoring Network in STEM (IMNIS) program and collaborates closely with industry partners across Australia. Dr. Sarker is a member of ACM, IEEE, and the Australian Information Security Association (AISA).

Professional Memberships

  • AISA - Australian Information Security Association (member)
  • ACM - Association for Computing Machinery (member)
  • IEEE - Institute of Electrical and Electronics Engineers (member)

Awards and Recognition

  • 2014 - Swinburne University Postgraduate Research Award, Australia.
  • 2016 - IEEE Award, IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), Canada.
  • 2025 - ELSEVIER Journal Award, ICT Express Best Paper Runner Award, Portugal.
  • 2025 - Ranked by ScholarGPS
  • 2025 - Listed in the world's top 2% of most-cited scientists by Elsevier and Stanford University, USA.

Research Areas and Interests

  • Responsible AI, ML Algorithms, Data-Driven, Knowledge/Rule Mining, Agentic AI
  • Cybersecurity Analytics & Automation, Ethics & Trust, Digital Twins, Human-AI Teaming
  • Business Analytics & SMEs, Intelligent Decision-Making, AI/Data-Driven Transformation

Qualifications

  • Doctor of Philosophy, Swinburne University of Technology, 2018.

Research Outputs

2025

Journal Articles

  • Jafar, MT., Yang, L., Li, G., Doss, R., Mouzakis, K., Vasa, R., Janicke, H., Ibrahim, A., Mohsin, A., Sarker, MI., Moore, K., Camtepe, S., Goel, D. (2025). Mitigating malware prevalence in networks with arbitrary topologies: a Flip-It cyber game approach integrated with epidemic modeling. Information Sciences, 726(2026), 122753. https://doi.org/https://doi.org/10.1016/j.ins.2025.122753.
  • Yigit, Y., Ferrag, MA., Ghanem, MC., Sarker, MI., Maglaras, LA., Chrysoulas, C., Moradpoor, N., Tihanyi, N., Janicke, H. (2025). Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities. Sensors, 25(6), article number 1666. https://doi.org/10.3390/s25061666.
  • Hasan, M., Rahman, MS., Morshed Chowdhury, MJ., Sarker, MI. (2025). CNN Based Deep Learning Modeling with Explainability Analysis for Detecting Fraudulent Blockchain Transactions. Cyber Security and Applications, 3(2025), article number 100101. https://doi.org/10.1016/j.csa.2025.100101.
  • Hossain, S., Deb, K., Sakib, S., Sarker, MI. (2025). A hybrid deep learning framework for daily living human activity recognition with cluster-based video summarization. Multimedia Tools and Applications, 84(9), 6219-6272. https://doi.org/10.1007/s11042-024-19022-0.

2024

Books

Journal Articles

  • Hasan, M., Rahman, MS., Janicke, H., Sarker, MI. (2024). Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis. Blockchain: Research and Applications, 5(3), article number 100207. https://doi.org/10.1016/j.bcra.2024.100207.
  • Faruqui, N., Thatoi, P., Choudhary, R., Roncevic, I., Alqahtani, H., Sarker, MI., Khanam, S. (2024). AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization. IEEE Access, 12(2024), 195188-195203. https://doi.org/10.1109/ACCESS.2024.3519423.
  • Anwar, SS., Asaduzzaman, ., Sarker, MI. (2024). A Differential Privacy Aided Deepfed Intrusion Detection System For Iot Applications. Security and Privacy, 7(6), pp 18. https://doi.org/10.1002/spy2.445.
  • Sarker, MI., Janicke, H., Ferrag, MA., Abuadbba, A. (2024). Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures. Internet of Things, 25(2024), article number 101110. https://doi.org/10.1016/j.iot.2024.101110.
  • Zubair, M., Iqbal, M., Shil, A., Chowdhury, M., Moni, M., Sarker, MI. (2024). An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling. Annals of Data Science, 11(5), 1525-1544. https://doi.org/10.1007/s40745-022-00428-2.
  • Jamal, S., Wimmer, H., Sarker, MI. (2024). An Improved Transformer-Based Model For Detecting Phishing, Spam And Ham Emails: A Large Language Model Approach. Security and Privacy, 7(5), article number e402. https://doi.org/10.1002/spy2.402.
  • Zubair, M., Janicke, H., Mohsin, A., Maglaras, L., Sarker, MI. (2024). Automated Sensor Node Malicious Activity Detection with Explainability Analysis. Sensors, 24(12), Article number 3712. https://doi.org/https://doi.org/10.3390/s24123712.
  • Hossain, GM., Deb, K., Janicke, H., Sarker, MI. (2024). PDF Malware Detection: Toward Machine Learning Modeling With Explainability Analysis. IEEE Access, 12(2024), 13833-13859. https://doi.org/10.1109/ACCESS.2024.3357620.
  • Sarker, MI., Janicke, H., Mohsin, A., Gill, A., Maglaras, L. (2024). Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects. ICT Express, 10(4), 935-958. https://doi.org/https://doi.org/10.1016/j.icte.2024.05.007.
  • Islam, MN., Islam, MS., Shourav, NH., Rahman, I., Faisal, FA., Islam, MM., Sarker, MI. (2024). Exploring post-COVID-19 health effects and features with advanced machine learning techniques. Scientific Reports, 14(1), article number 9884. https://doi.org/10.1038/s41598-024-60504-w.
  • Sarker, MI. (2024). LLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling. Discover Artificial Intelligence, 4(1), article number 40. https://doi.org/10.1007/s44163-024-00129-0.
  • Saifullah, K., Khan, MI., Jamal, S., Sarker, MI. (2024). Cyberbullying Text Identification: A Deep Learning and Transformer-based Language Modeling Approach. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 11(1), 1-12. https://doi.org/10.4108/EETINIS.V11I1.4703.

Conference Publications

  • Hossain, GS., Deb, K., Sarker, MI. (2024). An Enhanced Feature-Based Hybrid Approach for Adversarial PDF Malware Detection. Proceedings - 6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024 (101-106). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEEICT62016.2024.10534412.
  • Tasnim, N., Noor, KR., Islam, M., Huda, MN., Sarker, MI. (2024). A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024 (1060-1064). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETSIS61505.2024.10459494.
  • Sarker, MI., Janicke, H., Maglaras, L., Camtepe, S. (2024). Data-Driven Intelligence Can Revolutionize Today’s Cybersecurity World: A Position Paper. Communications in Computer and Information Science (302-316). Springer. https://doi.org/10.1007/978-3-031-48855-9_23.

2023

Journal Articles

  • Sarker, MI. (2023). Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Annals of Data Science, 10(6), 1473-1498. https://doi.org/10.1007/s40745-022-00444-2.
  • Rahman, MA., Aonty, SS., Deb, K., Sarker, MI. (2023). Attention-Based Human Age Estimation from Face Images to Enhance Public Security. Data, 8(10), article number 145. https://doi.org/10.3390/data8100145.
  • Hossain, MR., Hoque, MM., Siddique, N., Sarker, MI. (2023). Covtinet: Covid Text Identification Network Using Attention-Based Positional Embedding Feature Fusion. Neural Computing and Applications, 35(18), 13503-13527. https://doi.org/10.1007/s00521-023-08442-y.
  • Rahman, MM., Khan, NI., Sarker, MI., Ahmed, M., Islam, MN. (2023). Leveraging machine learning to analyze sentiment fromCOVID-19 tweets: A global perspective. Engineering Reports, 5(3), article number e12572. https://doi.org/10.1002/eng2.12572.
  • Sami, A., Sakib, S., Deb, K., Sarker, MI. (2023). Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management. Algorithms, 16(9), article number 452. https://doi.org/10.3390/a16090452.
  • Sarker, MI., Khan, A., Abushark, Y., Alsolami, F. (2023). Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions. Mobile Networks and Applications, 28(1), 296-312. https://doi.org/10.1007/s11036-022-01937-3.
  • Islam, U., Haque, E., Alsalman, D., Islam, M., Moni, M., Sarker, MI. (2023). A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors. Annals of Data Science, 10(6), 1607-1634. https://doi.org/10.1007/s40745-022-00381-0.
  • Sarker, MI. (2023). Multi-aspects AI-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview. Security and Privacy, 6(5), article number e295. https://doi.org/10.1002/spy2.295.

Conference Publications

  • Jony, A., Islam, MN., Sarker, MI. (2023). Unveiling DNS Spoofing Vulnerabilities: An Ethical Examination Within Local Area Networks. 2023 26th International Conference on Computer and Information Technology, ICCIT 2023 (6 pages). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIT60459.2023.10441649.

2022

Journal Articles

  • Ripan, R., Islam, M., Alqahtani, H., Sarker, MI. (2022). Effectively predicting cyber-attacks through isolation forest learning-based outlier detection. Security and Privacy, 5(3), TBD. https://doi.org/10.1002/spy2.212.
  • Furhad, H., Chakrabortty, R., Ryan, M., Uddin, J., Sarker, I., Sarker, MI. (2022). A hybrid framework for detecting structured query language injection attacks in web-based applications. International Journal of Electrical and Computer Engineering, 12(5), 5405-5414. https://doi.org/10.11591/ijece.v12i5.pp5405-5414.
  • Hossain, M., Arefin, M., Sarker, MI., Kowsher, M., Dhar, P., Koshiba, T. (2022). CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation. IEEE Access, 10(2022), 36299-36310. https://doi.org/10.1109/ACCESS.2022.3161941.
  • Sarker, MI. (2022). Smart City Data Science: Towards data-driven smart cities with open research issues. Internet of Things, 19(2022), article number 100528. https://doi.org/10.1016/j.iot.2022.100528.
  • Gomasta, S., Dhali, A., Anwar, M., Sarker, MI. (2022). Query-oriented topical influential users detection for top-k trending topics in twitter. Applied Intelligence, 52(12), 13415-13434. https://doi.org/10.1007/s10489-022-03582-5.

2021

Books

  • Sarker, MI., Colman, A., Han, J., Watters, P. (2021). Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making. Springer. https://doi.org/10.1007/978-3-030-88530-4.

Journal Articles

  • Hossain, M., Hoque, M., Siddique, N., Sarker, MI. (2021). Bengali text document categorization based on very deep convolution neural network. Expert Systems with Applications, 184(2021), article number 115394. https://doi.org/10.1016/j.eswa.2021.115394.
  • Hossain, M., Hoque, M., Dewan, M., Siddique, N., Islam, M., Sarker, MI. (2021). Authorship classification in a resource constraint language using convolutional neural networks. IEEE Access, 9(2021), 100319-100338. https://doi.org/10.1109/ACCESS.2021.3095967.
  • Das, B., Anwar, M., Bhuiyan, M., Sarker, MI., Alyami, S., Moni, M. (2021). Attribute Driven Temporal Active Online Community Search. IEEE Access, 9(2021), 93976-93989. https://doi.org/10.1109/ACCESS.2021.3093368.
  • Sarker, MI. (2021). CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things, 14(2021), article number 100393. https://doi.org/10.1016/j.iot.2021.100393.
  • Sarker, MI., Hoque, M., Uddin, M., Alsanoosy, T. (2021). Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Networks and Applications, 26(1), 285-303. https://doi.org/10.1007/s11036-020-01650-z.
  • Sarker, MI. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), article number 160. https://doi.org/10.1007/s42979-021-00592-x.
  • Sarker, MI. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6), article number 420. https://doi.org/10.1007/s42979-021-00815-1.

Conference Publications

  • Ulfath, R., Alqahtani, H., Hammoudeh, M., Sarker, MI. (2021). Hybrid CNN-GRU Framework with Integrated Pre-trained Language Transformer for SMS Phishing Detection. ACM International Conference Proceeding Series (244-251). Association for Computing Machinery. https://doi.org/10.1145/3508072.3508109.

2020

Journal Articles

  • Islam, M., Inan, T., Rafi, S., Akter, S., Sarker, I., Najmul Islam, A., Sarker, MI. (2020). A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic. IEEE Transactions on Artificial Intelligence, 1(3), 258-270. https://doi.org/10.1109/TAI.2021.3062771.
  • Sarker, MI., Kayes, A., Badsha, S., Alqahtani, H., Watters, P., Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7(1), article number 41. https://doi.org/10.1186/s40537-020-00318-5.
  • Kayes, A., Kalaria, R., Sarker, MI., Islam, M., Watters, P., Ng, A., Hammoudeh, M., Badsha, S., Kumara, I. (2020). A survey of context-aware access control mechanisms for cloud and fog networks: Taxonomy and open research issues. Sensors, 20(9), article number 2464. https://doi.org/10.3390/s20092464.
  • Sarker, MI., Kayes, A. (2020). ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications, 168(2020), article number 102762. https://doi.org/10.1016/j.jnca.2020.102762.

Conference Publications

  • Hossain, S., Tanjil, M., Ali, M., Islam, M., Islam, M., Mobassirin, S., Sarker, MI., Islam, S. (2020). Rice Leaf Diseases Recognition Using Convolutional Neural Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (299-314). Springer. https://doi.org/10.1007/978-3-030-65390-3_23.
  • Dhali, A., Gomasta, S., Anwar, M., Sarker, MI. (2020). Attribute-driven Topical Influential Users Detection in Online Social Networks. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 (Article number 9411637). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSDE50874.2020.9411637.

2019

Journal Articles

  • Sarker, MI. (2019). A machine learning based robust prediction model for real-life mobile phone data. Internet of Things, 5(2019), 180-193. https://doi.org/10.1016/j.iot.2019.01.007.
  • Sarker, MI. (2019). Context-aware rule learning from smartphone data: survey, challenges and future directions. Journal of Big Data, 6(1), article number 95. https://doi.org/10.1186/s40537-019-0258-4.
  • Sarker, MI., Kayes, A., Watters, P. (2019). Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data, 6(1), article number 57. https://doi.org/10.1186/s40537-019-0219-y.
  • Sarker, MI., Colman, A., Han, J. (2019). RecencyMiner: mining recency-based personalized behavior from contextual smartphone data. Journal of Big Data, 6(1), article number 49. https://doi.org/10.1186/s40537-019-0211-6.

2018

Journal Articles

  • Sarker, MI., Colman, A., Kabir, M., Han, J. (2018). Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior. The Computer Journal, 61(3), 349-368. https://doi.org/10.1093/comjnl/bxx082.

Conference Publications

  • Sarker, MI. (2018). BehavMiner: Mining User Behaviors from Mobile Phone Data for Personalized Services. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 (452-453). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PERCOMW.2018.8480325.
  • Sarker, MI., Salim, F. (2018). Mining user behavioral rules from smartphone data through association analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (450-461). Springer Verlag. https://doi.org/10.1007/978-3-319-93034-3_36.
  • Sarker, MI., Kabir, M., Colman, A., Han, J. (2018). An Improved Naive Bayes Classifier-Based Noise Detection Technique for Classifying User Phone Call Behavior. Communications in Computer and Information Science (72-85). Springer Verlag. https://doi.org/10.1007/978-981-13-0292-3_5.

2017

Conference Publications

  • Sarker, MI., Colman, A., Kabir, M., Han, J. (2017). An effective call prediction model based on noisy mobile phone data. UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (193-196). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123024.3123088.
  • Sarker, MI., Kabir, M., Colman, A., Han, J. (2017). Understanding recency-based behavior model for individual mobile phone users. UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (916-921). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123024.3124570.
  • Sarker, MI., Kabir, M., Colman, A., Han, J. (2017). Identifying Recent Behavioral Data Length in Mobile Phone Log. ACM International Conference Proceeding Series (545-546). Association for Computing Machinery. https://doi.org/10.1145/3144457.3144506.
  • Sarker, MI., Kabir, M., Colman, A., Han, J. (2017). An Approach to Modeling Call Response Behavior on Mobile Phones Based on Multi-Dimensional Contexts. Proceedings - 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems, MOBILESoft 2017 (91-95). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MOBILESoft.2017.38.

2016

Conference Publications

  • Sarker, MI., Colman, A., Kabir, M., Han, J. (2016). Phone call log as a context source to modeling individual user behavior. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (630-634). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971592.
  • Sarker, MI., Colman, A., Kabir, M., Han, J. (2016). Behavior-oriented time segmentation for mining individualized rules of mobile phone users. 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 (488-497). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2016.60.

Research Projects

  • Next Generation Policing: Effective and Ethical Use of AI and Data Science in Law Enforcement, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Next Generation Graduates Program, 2024 ‑ 2029, $35,000.
  • Securing DER Stability via a Trusted Sovereign Monitor for EV Charging Systems., Commonwealth Scientific and Industrial Research Organisation (CSIRO), Tender, 2025 ‑ 2026, $163,367.

Research Student Supervision

Current Supervisions

Co-principal Supervisor

  • Master of Computing and Security by Research, CHALLENGES IN GOVERNANCE, RISK, AND COMPLIANCE AND THE POTENTIAL IMPACT OF ARTIFICAL INTELLIGENCE
  • Doctor of Philosophy, Computational Trust in Online User Generated Content using Artificial Intelligence

Completed Supervisions

Principal Supervisor

  • Topic Identification and Sentiment Analysis of COVID-19 Tweets Using Deep Learning
  • Anomaly Detection in Blockchain Transactions uning Machine Learning with Explainability Analysis
Skip to top of page