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Dr Iqbal H Sarker

Post Doctoral Research Fellow

Staff Member Details
Email: m.sarker@ecu.edu.au
Campus: Joondalup  
Room: JO23.320  
ORCID iD: https://orcid.org/0000-0003-1740-5517

Iqbal is a Post-doctoral Research Fellow of the Cyber Security cooperative Research Centre (CSCRC) in association with the ECU Security Research Institute.

Background

Dr. Iqbal H. Sarker received his Ph.D. in Computer Science from Swinburne University of Technology, Melbourne, Australia in 2018. Later, he has also been selected by Global Talent Independent, Australia. Now he is working as a post-doctoral research fellow at Cybersecurity CRC, Edith Cowan University, Australia through academia-industry collaboration including CSIRO’s Data61. His professional and research interests include Cybersecurity, AI/XAI-based Modeling, Machine Learning, Data Science and Behavioral Analytics, Digital Twin, Critical Infrastructure Security and Resilience. He has published a good number of 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 a research monograph BOOK titled "Context-Aware Machine Learning and Mobile Data Analytics: Automated Rule-based Services with Intelligent Decision-Making", published by Springer Nature, Switzerland, 2021. Dr. Sarker has also been listed in the world's TOP 2% of most-cited scientists, published by Elsevier & Stanford University, USA, 2020~Now. He is a member of IEEE, ACM and Australian Information Security Association.

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.
  • 2022 - Listed in the world's top 2% of most-cited scientists by Elsevier and Stanford University, USA.

Research Areas and Interests

  • Cybersecurity Automation and Threat Intelligence
  • Machine Learning & AI/XAI-based Modelling
  • Data Science and Data-Driven Decision Making
  • Behavioural and Predictive Analytics
  • Digital Twin, Smart Cities and Critical Infrastructure Security

Qualifications

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

Research Outputs

2024

Journal Articles

  • 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.
  • 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.

Conference Publications

  • 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

  • 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.
  • 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.
  • 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.
  • 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.

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

  • 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.
  • 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.
  • Sarker, MI., Khan, A., Abushark, Y., Alsolami, F. (2022). Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions. Mobile Networks and Applications, 2022(Article in Press), TBD. https://doi.org/10.1007/s11036-022-01937-3.
  • 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.
  • 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.
  • Zubair, M., Iqbal, M., Shil, A., Chowdhury, M., Moni, M., Sarker, MI. (2022). An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling. Annals of Data Science, 2022(Article in Press), TBD. https://doi.org/10.1007/s40745-022-00428-2.
  • 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). Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Annals of Data Science, 2022(Article in Press), TBD. https://doi.org/10.1007/s40745-022-00444-2.
  • Islam, U., Haque, E., Alsalman, D., Islam, M., Moni, M., Sarker, MI. (2022). A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors. Annals of Data Science, 2022(Article in Press), TBD. https://doi.org/10.1007/s40745-022-00381-0.

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

  • 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., 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.
  • 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.
  • 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.

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

  • 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.
  • 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. (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.
  • 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.

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). 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., 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.
  • 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., 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.

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., 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.
  • 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. (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.

2017

Conference Publications

  • 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.
  • 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.

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 Student Supervision

Current Supervisions

No data available

Completed Supervisions

Principal Supervisor

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