activity
20182021
most citedMalware Detection using Machine Learning and Deep Learning

124 citations · 251 across the 9 of their papers we have counts for

collaborators

13 papers

cs.CR20219 cited

ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Advanced metamorphic malware and ransomware, by using obfuscation, could alter their internal structure with every attack. If such malware could intrude even into any of the IoT ne…

cs.CR20212 cited

LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Long-Short-Term-Memory (LSTM) networks have shown great promise in artificial intelligence (AI) based language modeling. Recently, LSTM networks have also become popular for design…

cs.CR20214 cited

DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for…

cs.CR20212 cited

Identification of Significant Permissions for Efficient Android Malware Detection

Hemant Rathore, Sanjay K. Sahay, Ritvik Rajvanshi +1

Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/i…

cs.CR20211 cited

Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering

Hemant Rathore, Sanjay K. Sahay, Shivin Thukral +1

Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection me…

cs.CR202116 cited

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation…