activity
20122025
most citedMalware Detection using Machine Learning and Deep Learning

124 citations · 605 across the 39 of their papers we have counts for

collaborators

41 papers

cs.CR2025★ 3 cited

Modern Hardware Security: A Review of Attacks and Countermeasures

Jyotiprakash Mishra, Sanjay K. Sahay

With the exponential rise in the use of cloud services, smart devices, and IoT devices, advanced cyber attacks have become increasingly sophisticated and ubiquitous. Furthermore, t…

cs.CR2022★ 46 cited

Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review

Mohit Sewak, Sanjay K. Sahay, Hemant Rathore

The cybersecurity threat landscape has lately become overly complex. Threat actors leverage weaknesses in the network and endpoint security in a very coordinated manner to perpetua…

cs.CR2021★ 1 cited

Privacy-Preserving Mutual Authentication and Key Agreement Scheme for Multi-Server Healthcare System

Trupil Limbasiya, Sanjay K. Sahay, Bharath Sridharan

The usage of different technologies and smart devices helps people to get medical services remotely for multiple benefits. Thus, critical and sensitive data is exchanged between a…

cs.CR2021★ 9 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.CR2021★ 2 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.CR2021★ 4 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…