| A comparison of static, dynamic, and hybrid analysis for malware detection A Damodaran, FD Troia, CA Visaggio, TH Austin, M Stamp Journal of Computer Virology and Hacking Techniques 13, 1-12, 2017 | 483 | 2017 |
| Transfer learning for image-based malware classification N Bhodia, P Prajapati, F Di Troia, M Stamp arXiv preprint arXiv:1903.11551, 2019 | 112 | 2019 |
| Deep Learning versus Gist Descriptors for Image-based Malware Classification. S Yajamanam, VRS Selvin, F Di Troia, M Stamp Icissp, 553-561, 2018 | 92 | 2018 |
| Detecting malware evolution using support vector machines M Wadkar, F Di Troia, M Stamp Expert Systems with Applications 143, 113022, 2020 | 84 | 2020 |
| Clustering for malware classification S Pai, FD Troia, CA Visaggio, TH Austin, M Stamp Journal of Computer Virology and Hacking Techniques 13, 95-107, 2017 | 78 | 2017 |
| Support vector machines and malware detection T Singh, F Di Troia, VA Corrado, TH Austin, M Stamp Journal of Computer Virology and Hacking Techniques 12, 203-212, 2016 | 74 | 2016 |
| Feature analysis of encrypted malicious traffic AS Shekhawat, F Di Troia, M Stamp Expert Systems with Applications 125, 130-141, 2019 | 69 | 2019 |
| Static and dynamic analysis of android malware A Kapratwar, F Di Troia, M Stamp International Workshop on FORmal methods for Security Engineering 2, 653-662, 2017 | 62 | 2017 |
| Malware detection using dynamic birthmarks S Vemparala, F Di Troia, VA Corrado, TH Austin, M Stamo Proceedings of the 2016 ACM on international workshop on security and …, 2016 | 52 | 2016 |
| Clustering versus SVM for malware detection U Narra, FD Troia, VA Corrado, TH Austin, M Stamp Journal of Computer Virology and Hacking Techniques 12, 213-224, 2016 | 42 | 2016 |
| Convolutional neural networks for image spam detection T Sharmin, F Di Troia, K Potika, M Stamp Information Security Journal: A Global Perspective 29 (3), 103-117, 2020 | 29 | 2020 |
| Support vector machines for image spam analysis A Chavda, K Potika, F Di Troia, M Stamp | 24 | 2018 |
| Classic cryptanalysis using hidden Markov models R Vobbilisetty, F Di Troia, RM Low, CA Visaggio, M Stamp Cryptologia 41 (1), 1-28, 2017 | 22 | 2017 |
| Multifamily malware models S Basole, F Di Troia, M Stamp Journal of Computer Virology and Hacking Techniques 16, 79-92, 2020 | 20 | 2020 |
| An analysis of Android adware S Suresh, F Di Troia, K Potika, M Stamp Journal of Computer Virology and Hacking Techniques 15, 147-160, 2019 | 20 | 2019 |
| Hidden Markov models with random restarts versus boosting for malware detection A Raghavan, F Di Troia, M Stamp Journal of Computer Virology and Hacking Techniques 15, 97-107, 2019 | 20 | 2019 |
| A comparative analysis of android malware N Chavan, F Di Troia, M Stamp arXiv preprint arXiv:1904.00735, 2019 | 20 | 2019 |
| Malware classification with word2vec, hmm2vec, bert, and elmo AS Kale, V Pandya, F Di Troia, M Stamp Journal of Computer Virology and Hacking Techniques 19 (1), 1-16, 2023 | 18 | 2023 |
| Malware classification using long short-term memory models D Dang, F Di Troia, M Stamp arXiv preprint arXiv:2103.02746, 2021 | 18 | 2021 |
| Clickbait detection for youtube videos R Gothankar, FD Troia, M Stamp Artificial intelligence for cybersecurity, 261-284, 2022 | 14 | 2022 |
