| Malware modeling detection system and method for mobile platforms G Tuvell, D Venugopal, G Hu US Patent 8,321,941, 2012 | 617 | 2012 |
| Malware detection system and method for mobile platforms G Tuvell, D Venugopal US Patent 9,104,871, 2015 | 549 | 2015 |
| Non-signature malware detection system and method for mobile platforms G Tuvell, D Venugopal, M Pfefferle US Patent 8,312,545, 2012 | 500 | 2012 |
| Malware detection system and method for compressed data on mobile platforms G Tuvell, D Venugopal US Patent 9,009,818, 2015 | 338 | 2015 |
| Wireless intrusion prevention system and method G Hu, D Venugopal, S Bhardwaj US Patent 8,087,085, 2011 | 268 | 2011 |
| Detecting review manipulation on online platforms with hierarchical supervised learning N Kumar, D Venugopal, L Qiu, S Kumar Journal of Management Information Systems 35 (1), 350-380, 2018 | 216 | 2018 |
| System and method of malware sample collection on mobile networks G Tuvell, D Venugopal, G Hu US Patent 8,881,283, 2014 | 193 | 2014 |
| DDoS intrusion detection through machine learning ensemble S Das, AM Mahfouz, D Venugopal, S Shiva 2019 IEEE 19th international conference on software Quality, Reliability and …, 2019 | 115 | 2019 |
| Efficient signature based malware detection on mobile devices D Venugopal, G Hu Mobile Information Systems 4 (1), 33-49, 2008 | 102 | 2008 |
| Detecting anomalous online reviewers: An unsupervised approach using mixture models N Kumar, D Venugopal, L Qiu, S Kumar Journal of Management Information Systems 36 (4), 1313-1346, 2019 | 100 | 2019 |
| Comparative analysis of ML classifiers for network intrusion detection AM Mahfouz, D Venugopal, SG Shiva Fourth International Congress on Information and Communication Technology …, 2020 | 94 | 2020 |
| Ensemble classifiers for network intrusion detection using a novel network attack dataset A Mahfouz, A Abuhussein, D Venugopal, S Shiva Future Internet 12 (11), 180, 2020 | 90 | 2020 |
| Relieving the computational bottleneck: Joint inference for event extraction with high-dimensional features D Venugopal, C Chen, V Gogate, V Ng Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 85 | 2014 |
| On lifting the gibbs sampling algorithm D Venugopal, V Gogate Advances in Neural Information Processing Systems 25, 2012 | 68 | 2012 |
| Evidence-based clustering for scalable inference in markov logic D Venugopal, V Gogate Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 54 | 2014 |
| Joint inference for event coreference resolution J Lu, D Venugopal, V Gogate, V Ng Proceedings of COLING 2016, the 26th International Conference on …, 2016 | 50 | 2016 |
| Lifted MAP inference for Markov logic networks S Sarkhel, D Venugopal, P Singla, V Gogate Artificial intelligence and statistics, 859-867, 2014 | 49 | 2014 |
| Empirical evaluation of the ensemble framework for feature selection in ddos attack S Das, D Venugopal, S Shiva, FT Sheldon 2020 7th IEEE International Conference on Cyber Security and Cloud Computing …, 2020 | 48 | 2020 |
| Advances in lifted importance sampling V Gogate, A Jha, D Venugopal Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1910-1916, 2012 | 48 | 2012 |
| A holistic approach for detecting DDoS attacks by using ensemble unsupervised machine learning S Das, D Venugopal, S Shiva Advances in Information and Communication: Proceedings of the 2020 Future of …, 2020 | 40 | 2020 |
Vibhav GogateThe University of Texas at DallasZweryfikowany adres z utdallas.edu
