| Deep learning approach for intelligent intrusion detection system R Vinayakumar, M Alazab, KP Soman, P Poornachandran, A Al-Nemrat, ... Ieee Access 7, 41525-41550, 2019 | 1604 | 2019 |
| Robust intelligent malware detection using deep learning R Vinayakumar, M Alazab, KP Soman, P Poornachandran, ... IEEE access 7, 46717-46738, 2019 | 531 | 2019 |
| A framework for implementing TQM in higher education programs S Venkatraman Quality assurance in education 15 (1), 92-112, 2007 | 476 | 2007 |
| Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures. M Alazab, S Venkatraman, PA Watters, M Alazab AusDM 11, 171-182, 2011 | 302 | 2011 |
| A hybrid deep learning image-based analysis for effective malware detection S Venkatraman, M Alazab, R Vinayakumar Journal of Information Security and Applications 47, 377-389, 2019 | 244 | 2019 |
| Towards understanding malware behaviour by the extraction of API calls M Alazab, S Venkataraman, P Watters 2010 second cybercrime and trustworthy computing workshop, 52-59, 2010 | 227 | 2010 |
| Use of data visualisation for zero‐day malware detection S Venkatraman, M Alazab Security and Communication Networks 2018 (1), 1728303, 2018 | 151 | 2018 |
| Challenges and success factors of ERP systems in Australian SMEs S Venkatraman, K Fahd Systems 4 (2), 20, 2016 | 137 | 2016 |
| Cybercrime: The Case of Obfuscated Malware M Alazab, S Venkatraman, P Watters, M Alazab, A Alazab Proceedings of International Conference in Global Security Safety and …, 2011 | 117 | 2011 |
| Malware detection based on structural and behavioural features of API calls M Alazab, R Layton, S Venkataraman, P Watters School of Computer and Information Science, Security Research Centre, Edith …, 2010 | 105 | 2010 |
| Use of the Analytic Hierarchy Process for SelectingAutomated Manufacturing Systems RP Mohanty, S Venkataraman International Journal of Operations & Production Management 13 (8), 45-57, 1993 | 104 | 1993 |
| Performance Evaluation of E-Government Services Using Balanced Scorecard - An Empirical Study in Jordan S Al-Hyari, M Alazab, S Venkatraman, A Alazab International Journal of Benchmarking 20 (4), 512 - 536, 2013 | 94 | 2013 |
| Biometrics in banking security: a case study S Venkatraman, I Delpachitra Information Management & Computer Security 16 (4), 415-430, 2008 | 94 | 2008 |
| SQL versus NoSQL movement with big data analytics S Venkatraman, K Fahd, S Kaspi, R Venkatraman International Journal of Information Technology and Computer Science 8 (12 …, 2016 | 92 | 2016 |
| Challenges in recruitment and selection process: An empirical study SD Rozario, S Venkatraman, A Abbas Challenges 10 (2), 35, 2019 | 91 | 2019 |
| MTHAEL: Cross-architecture IoT malware detection based on neural network advanced ensemble learning D Vasan, M Alazab, S Venkatraman, J Akram, Z Qin IEEE Transactions on Computers 69 (11), 1654-1667, 2020 | 89 | 2020 |
| A hybrid wrapper-filter approach for malware detection M Alazab, S Huda, J Abawajy, MDR Islam, J Yearwood, S Venkatraman, ... Journal of networks 9 (11), 2878-2891, 2014 | 73 | 2014 |
| Improving employment outcomes of career and technical education students S Venkatraman, T de Souza-Daw, S Kaspi Higher Education, Skills and Work-Based Learning 8 (4), 469-483, 2018 | 70 | 2018 |
| Relationships among triple bottom line elements: Focus on integrating sustainable business practices S Venkatraman, RR Nayak Journal of Global Responsibility 6 (2), 195-214, 2015 | 66 | 2015 |
| Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature K Fahd, S Venkatraman, SJ Miah, K Ahmed Education and Information Technologies, 1-33, 2022 | 61 | 2022 |
