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    • Dr Mohiuddin Ahmed
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    • Dr Mohiuddin Ahmed

      Dr Mohiuddin Ahmed

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Dr Mohiuddin Ahmed

Lecturer

Contact Information Telephone: +61 8 6304 5121, Email: mohiuddin.ahmed@ecu.edu.au, Campus: Joondalup, Room: JO18309
Staff Member Details
Telephone: +61 8 6304 5121
Email: mohiuddin.ahmed@ecu.edu.au
Campus: Joondalup  
Room: JO18309  

 

Overview of role

Dr. Mohiuddin Ahmed is a Lecturer of Computing and Security discipline in the School of Science.

Current Teaching

  • CSI2108 Cryptographic Concepts
  • CSG3309 IT Security Management
  • CSI2107 Software Reverse Engineering

Background

Mohiuddin Ahmed attained his PhD in Computer Science from UNSW  Australia. His research expertise encompasses cyber security and machine learning, and covers a wide range of application domains. Mohiuddin holds over five years of data science and cyber security experience. He is currently working as a Lecturer in Computing and Security Sciences in the School of Science at Edith Cowan University, Australia. Prior to joining ECU, he served as a Lecturer in the Centre for Cyber Security and Games at Canberra Institute of Technology (CIT) and was also involved with CIT's Data Strategy Working Group.

He is currently exploring blockchain for ensuring security of healthcare devices. He is also involved in Cyber CRC projects. Mohiuddin has edited book on Data Analytics published by CRC press, USA. Previously, he has worked in the areas of text mining and predictive analytics in the artificial intelligence division at MIMOS, Malaysia. In PhD, he has made practical and theoretical contributions in big data analytics (summarization) for a number of application domains.

Mohiuddin's research has had a high impact on data analytics, critical infrastructure protection (IoT, smart grids), and information security against DoS attacks, false data injection attacks, etc. and health analytics (heart disease diagnosis). He has hands on experience of and certifications in Hadoop, Spark, R, SQL, RapidMiner and Data Science from IBM.

Currently, Mohiuddin is editorial advisory board member of Cambridge Scholars Publishing Group in UK and Associate Editor of the International Journal of Computers and Applications (Taylor & Francis Group).

Professional Memberships

  • IEEE
  • ACS
  • AISA

Awards and Recognition

National and International Awards

  • Australian Computer Society- Certified Technologist
  • UNSW Canberra - High Impact Research Publications Award
  • UNSW Canberra - PhD Thesis Writing Fellowship
  • UNSW Canberra - University International Postgraduate Award
  • UNSW Canberra - Postgraduate Research Support Scheme

University and National Teaching Awards

  • CIT Cyber Teaching Team Nomination for Staff Achievement Award, 2018

National and International Research Positions

  • 2016 – Senior Researcher at MIMOS, Malaysian National R&D.

Research Areas and Interests

  • Cyber Security
  • Data Analytics
  • Machine Learning
  • Big Data
  • Blockchain
  • Healthcare

Qualifications

  • Doctor of Philosophy, The University of New South Wales, 2016.

Research

Recent Research Grants

  • Developing autonomous solutions for building knowledge graphs to support educational outcomes in Virtuoso,  CingleVue Pty Ltd,  Scholarships to Support Industry Engagement PhD Project,  2019 - 2024,  $64,740.
  • Securing Internet of Medical Things against False Data Injection Attacks Using Blockchain,  Edith Cowan University,  ECU Early Career Researcher Grant - 2019,  2019 - 2020,  $25,000.

Recent Publications (within the last five years)

Book Chapters

  • Ahmed, M., Choudhury, S., Al-Turjman, F., (2019), Big Data Analytics for Intelligent Internet of Things. Artificial Intelligence in IoT, 107-127, Switzerland, Springer International Publishing, DOI: 10.1007/978-3-030-04110-6.
  • Bostami, B., Ahmed, M., Choudhury, S., (2019), False Data Injection Attacks in Internet of Things. Performability in Internet of Things, 47-58, Switzerland, Springer International Publishing, DOI: 10.1007/978-3-319-93557-7.
  • Ahmed, M., Barkat, A., (2018), Health Care Security Analytics. Data Analytics Concepts, Techniques, and Applications, 403-416, Boca Raton, USA, CRC Press, DOI: 10.1201/9780429446177.
  • Saeed, M., Ahmed, M., (2018), Project Management for Effective Data Analytics. Data Analytics Concepts, Techniques, and Applications, 219-234, Boca Raton, USA, CRC Press, DOI: 10.1201/9780429446177.
  • Hassan, MM., Ahmed, M., (2018), Blockchain in the Era of Industry 4.0. Data Analytics Concepts, Techniques, and Applications, 235-273, Boca Raton, USA, CRC Press, DOI: 10.1201/9780429446177.
  • Bostami, B., Ahmed, M., (2018), Intrusion Detection for Big Data. Data Analytics Concepts, Techniques, and Applications, 375-402, Boca Raton, USA, CRC Press, DOI: 10.1201/9780429446177.
  • Ahmed, M., Haque, N., (2018), Anomaly Detection and Big Data in IPTV Networks. IPTV Delivery Networks: Next Generation Architectures for Live and Video-on-Demand Services, Hoboken, USA, John Wiley & Sons.
  • Ahmed, M., (2017), Infrequent Pattern Identification in SCADA Systems Using Unsupervised Learning. Security Solutions and Applied Cryptography in Smart Grid Communications, 215-225, Hershey, USA, IGI Global , DOI: 10.4018/978-1-5225-1829-7.ch011.

Journal Articles

  • Ahmed, M., (2019), False Image Injection Prevention Using iChain. MDPI Applied Sciences , 9(20), 1-11, Switzerland, DOI: 10.3390/app9204328.
  • Ahmed, M., (2019), Intelligent Big Data Summarization for Rare Anomaly Detection. IEEE Access, 7(1), 68669 - 68677, DOI: 10.1109/ACCESS.2019.2918364.
  • Ahmed, M., (2019), Data summarization: a survey. Knowledge and Information Systems, 58(2), 249-273, DOI: 10.1007/s10115-018-1183-0.
  • Yang, J., Hasan Onik, MM., Lee, N., Ahmed, M., Kim, C., (2019), Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making. MDPI Applied Sciences , 9(7), 1-24, Switzerland, DOI: 10.3390/app9071370.
  • Aurisch, R., Ahmed, M., Barkat, A., (2019), An outlook at Agile methodologies for the independent games developer. International Journal of Computers and Applications, 1-7, Taylor & Francis, UK, DOI: 10.1080/1206212X.2019.1621463.
  • Ahmed, M., (2018), Collective Anomaly Detection Techniques for Network Traffic Analysis. Annals of Data Science, 5(4), 497-512, Heidelberg, Germany, DOI: 10.1007/s40745-018-0149-0.
  • Ahmed, M., Barkat, A., (2018), Infrequent pattern mining in smart healthcare environment using data summarization. Journal of Supercomputing, 74(10), 5041-5059, DOI: 10.1007/s11227-018-2376-8.
  • Ahmed, M., (2018), Reservoir-based network traffic stream summarization for anomaly detection. Pattern Analysis and Applications, 21(2), 579-599, DOI: 10.1007/s10044-017-0659-y.
  • Ahmed, M., (2017), An Unsupervised Approach of Knowledge Discovery from Big Data in Social Network. EAI Endorsed Transactions on Scalable Information Systems, 4(14), article no. e3, Belgium, DOI: 10.4108/eai.25-9-2017.153148.
  • Ahmed, M., (2017), Thwarting DoS Attacks: A Framework for Detection based on Collective Anomalies and Clustering. Computer, 50(9), 76-82, DOI: 10.1109/MC.2017.3571051.
  • Ahmed, M., Mahmood, A., Islam, MR., (2016), A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 55(1 February 2016), 278-288, DOI: 10.1016/j.future.2015.01.001.
  • Ahmed, M., Mahmood, A., Hu, J., (2016), A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60(1 January 2016), 19-31, DOI: 10.1016/j.jnca.2015.11.016.
  • Ahmed, M., Mahmood, A., Maher, M., (2015), An Efficient Technique for Network Traffic Summarization using Multiview Clustering and Statistical Sampling. EAI Endorsed Transactions on Scalable Information Systems, 2(5), article no. e4, Belgium, DOI: 10.4108/sis.2.5.e4.
  • Ahmed, M., Mahmood, A., (2015), Novel Approach for Network Traffic Pattern Analysis using Clustering-based Collective Anomaly Detection. Annals of Data Science, 2(1), 111-130, Heidelberg, Germany, DOI: 10.1007/s40745-015-0035-y.
  • Ahmed, M., Anwar, A., Mahmood, A., Shah, Z., Maher, M., (2015), An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2(3), article no. e5, Belgium, DOI: 10.4108/inis.2.3.e5.

Conference Publications

  • Saeed, M., Ahmed, M., (2019), Evaluation Metrics for Big Data Project Management. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 256(July 11-11, 2018), 139-142, Canada, Springer, Cham, DOI: 10.1007/978-3-030-05928-6_14.
  • Ahmed, M., Pathan, AK., (2019), Investigating Deep Learning for Collective Anomaly Detection - An Experimental Study. Communications in Computer and Information Science (CCIS), 969(Sep. 19-22, 2018), 211--219, Singapore, Springer, DOI: 10.1007/978-981-13-5826-5_15.
  • Furhad, MH., Ahmed, M., Barkat, A., (2019), A Study on Monitoring Coastal Areas for Having a Better Underwater Surveillance Perspective. Algorithms for Intelligent Systems, 163-174, Singapore, Springer, Singapore, DOI: 10.1007/978-981-13-7564-4_14.
  • Khandaker, S., Hussain, A., Ahmed, M., (2019), Effectiveness of Hard Clustering Algorithms for Securing Cyber Space. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 256(July 11-11, 2018), 113-120, Canada, Springer, Cham, DOI: 10.1007/978-3-030-05928-6_11.
  • Ahmed, M., Ullah, A., (2018), False Data Injection Attacks in Healthcare. Data Mining. AusDM 2017. Communications in Computer and Information Science, vol 845, 845, , 192-202, Singapore, Springer, DOI: 10.1007/978-981-13-0292-3_12.
  • Ahmed, M., Choudhury, N., Uddin, S., (2017), Anomaly detection on big data in financial markets. ASONAM '17 Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 998-1001, New York, NY, USA, ACM, DOI: 10.1145/3110025.3119402.
  • Anwar, A., Mahmood, A., Ahmed, M., (2015), False Data Injection Attack Targeting the LTC Transformers to Disrupt Smart Grid Operation. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 153(24th–26th Sep 2014), 252-266, Cham, Switzerland, Springer, Cham, DOI: 10.1007/978-3-319-23802-9_20.
  • Ahmed, M., Mahmood, A., Maher, M., (2015), Heart Disease Diagnosis Using Co-clustering. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 139(September 25-26, 2014), 61-70, Cham, Switzerland, Springer, Cham, DOI: 10.1007/978-3-319-16868-5_6.
  • Ahmed, M., Mahmood, A., Maher, M., (2015), An Efficient Approach for Complex Data Summarization Using Multiview Clustering. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 139(September 25-26, 2014), 38-47, Cham, Switzerland, Springer, Cham, DOI: 10.1007/978-3-319-16868-5_4.
  • Ahmed, M., Mahmood, A., Maher, M., (2015), A Novel Approach for Network Traffic Summarization. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 139(September 25-26, 2014), 51-60, Cham, Switzerland, Springer, Cham, DOI: 10.1007/978-3-319-16868-5_5.
  • Ahmed, M., Mahmood, A., (2015), Network Traffic Pattern Analysis Using Improved Information Theoretic Co-clustering Based Collective Anomaly Detection. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 153(September 24-26, 2014), 204-219, Cham, Switzerland, Springer, Cham, DOI: 10.1007/978-3-319-23802-9_17.
  • Ahmed, M., Mahmood, A., (2014), Network traffic analysis based on collective anomaly detection. 2014 9th IEEE Conference on Industrial Electronics and Applications, 1141–1146, Online, IEEE.
  • Ahmed, M., Mahmood, A., (2014), Clustering based semantic data summarization technique: A new approach. 2014 9th IEEE Conference on Industrial Electronics and Applications, 1780-1785, Online, IEEE.
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