| Survey on malware detection methods P Vinod, R Jaipur, V Laxmi, M Gaur Proceedings of the 3rd Hackers’ Workshop on computer and internet security …, 2009 | 247 | 2009 |
| Medusa: Metamorphic malware dynamic analysis usingsignature from api VP Nair, H Jain, YK Golecha, MS Gaur, V Laxmi Proceedings of the 3rd International Conference on Security of Information …, 2010 | 122 | 2010 |
| A machine learning based approach to detect malicious android apps using discriminant system calls P Vinod, A Zemmari, M Conti Future Generation Computer Systems 94, 333-350, 2019 | 85 | 2019 |
| Mining control flow graph as API call-grams to detect portable executable malware P Faruki, V Laxmi, MS Gaur, P Vinod Proceedings of the Fifth International Conference on Security of Information …, 2012 | 72 | 2012 |
| Malware detection employed by visualization and deep neural network A Pinhero, ML Anupama, P Vinod, CA Visaggio, N Aneesh, S Abhijith, ... Computers & Security 105, 102247, 2021 | 67 | 2021 |
| Droid permission miner: Mining prominent permissions for Android malware analysis AM Aswini, P Vinod The Fifth International Conference on the Applications of Digital …, 2014 | 59 | 2014 |
| SysDroid: a dynamic ML-based android malware analyzer using system call traces A Ananya, A Aswathy, TR Amal, PG Swathy, P Vinod, S Mohammad Cluster Computing 23 (4), 2789-2808, 2020 | 58 | 2020 |
| Identification of malicious android app using manifest and opcode features MV Varsha, P Vinod, KA Dhanya Journal of Computer Virology and Hacking Techniques 13, 125-138, 2017 | 58 | 2017 |
| Scheduling distributed energy resource operation and daily power consumption for a smart building to optimize economic and environmental parameters Z Pooranian, JH Abawajy, V P, M Conti Energies 11 (6), 1348, 2018 | 56 | 2018 |
| MOMENTUM: MetamOrphic malware exploration techniques using MSA signatures P Vinod, V Laxmi, MS Gaur, G Chauhan 2012 International Conference on Innovations in Information Technology (IIT …, 2012 | 49 | 2012 |
| Deep Learning Techniques for Android Botnet Detection SY Yerima, MK Alzaylaee, A Shajan, V P Electronics 10 10 (4), 519, 2021 | 48 | 2021 |
| A machine learning approach for linux malware detection KA Asmitha, P Vinod 2014 international conference on issues and challenges in intelligent …, 2014 | 46 | 2014 |
| Sentiment analysis using deep learning PC Shilpa, R Shereen, S Jacob, P Vinod 2021 Third International conference on intelligent communication …, 2021 | 45 | 2021 |
| Detection of Tor traffic using deep learning D Sarkar, P Vinod, SY Yerima 17th ACS/IEEE International Conference on Computer Systems and Applications …, 2020 | 36 | 2020 |
| Can machine learning model with static features be fooled: an adversarial machine learning approach R Taheri, R Javidan, M Shojafar, P Vinod, M Conti Cluster computing 23, 3233-3253, 2020 | 34 | 2020 |
| Secure brain-to-brain communication with edge computing for assisting post-stroke paralyzed patients S Rajesh, V Paul, VG Menon, S Jacob, P Vinod IEEE Internet of Things Journal 7 (4), 2531-2538, 2019 | 32 | 2019 |
| Static CFG analyzer for metamorphic Malware code P Vinod, V Laxmi, MS Gaur, G Kumar, YS Chundawat Proceedings of the 2nd International Conference on Security of Information …, 2009 | 30 | 2009 |
| Behavioural detection with API call-grams to identify malicious PE files. P Faruki, V Laxmi, MS Gaur, P Vinod SECURIT, 85-91, 2012 | 26 | 2012 |
| Reform: Relevant features for malware analysis P Vinod, V Laxmi, MS Gaur 2012 26th international conference on advanced information networking and …, 2012 | 26 | 2012 |
| Unknown metamorphic malware detection: Modelling with fewer relevant features and robust feature selection techniques J Kuriakose, P Vinod IAENG International Journal of Computer Science 42 (2), 139-151, 2015 | 25 | 2015 |
Vijay LaxmiProfessor (Computer Science and Engineering), Malaviya National Institute of Technology JaipurVerified email at mnit.ac.in
