UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) N Moustafa, J Slay 2015 military communications and information systems conference (MilCIS), 1-6, 2015 | 3048 | 2015 |
The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set N Moustafa, J Slay Information Security Journal: A Global Perspective 25 (1-3), 18-31, 2016 | 1023 | 2016 |
Lessons learned from the maroochy water breach J Slay, M Miller International conference on critical infrastructure protection, 73-82, 2007 | 915 | 2007 |
A holistic review of network anomaly detection systems: A comprehensive survey N Moustafa, J Hu, J Slay Journal of Network and Computer Applications 128, 33-55, 2019 | 318 | 2019 |
The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems N Moustafa, J Slay 2015 4th international workshop on building analysis datasets and gathering …, 2015 | 270 | 2015 |
Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks N Moustafa, J Slay, G Creech IEEE Transactions on Big Data 5 (4), 481-494, 2017 | 261 | 2017 |
Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling W Haider, J Hu, J Slay, BP Turnbull, Y Xie Journal of Network and Computer Applications 87, 185-192, 2017 | 216 | 2017 |
Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models N Moustafa, G Creech, J Slay Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies …, 2017 | 192 | 2017 |
Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques N Koroniotis, N Moustafa, E Sitnikova, J Slay Mobile Networks and Management: 9th International Conference, MONAMI 2017 …, 2018 | 185 | 2018 |
A hybrid feature selection for network intrusion detection systems: Central points N Moustafa, J Slay arXiv preprint arXiv:1707.05505, 2017 | 133 | 2017 |
Validation and verification of computer forensic software tools—Searching Function Y Guo, J Slay, J Beckett digital investigation 6, S12-S22, 2009 | 121 | 2009 |
Evaluating host-based anomaly detection systems: Application of the one-class SVM algorithm to ADFA-LD M Xie, J Hu, J Slay 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery …, 2014 | 94 | 2014 |
Digital forensics: Validation and verification in a dynamic work environment J Beckett, J Slay 2007 40th Annual Hawaii International Conference on System Sciences (HICSS …, 2007 | 73 | 2007 |
Anomaly detection system using beta mixture models and outlier detection N Moustafa, G Creech, J Slay Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2017 …, 2018 | 71 | 2018 |
Recovery of skype application activity data from physical memory M Simon, J Slay 2010 International Conference on Availability, Reliability and Security, 283-288, 2010 | 71 | 2010 |
Money laundering and terrorism financing in virtual environments: a feasibility study A SM Irwin, J Slay, KK Raymond Choo, L Lui Journal of Money Laundering Control 17 (1), 50-75, 2014 | 68 | 2014 |
Generalized outlier gaussian mixture technique based on automated association features for simulating and detecting web application attacks N Moustafa, G Misra, J Slay IEEE Transactions on Sustainable Computing 6 (2), 245-256, 2018 | 67 | 2018 |
Information technology security and risk management J Slay, A Koronios Wiley, 2006 | 65 | 2006 |
Creating novel features to anomaly network detection using DARPA-2009 data set N Moustaf, J Slay Proceedings of the 14th European Conference on Cyber Warfare and Security …, 2015 | 47 | 2015 |
Mobile device forensics: A snapshot C Tassone, B Martini, KKR Choo, J Slay Trends and Issues in Crime and Criminal Justice, 1-7, 2013 | 38 | 2013 |