124 citations · 423 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
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…
Robust Android Malware Detection System against Adversarial Attacks using Q-Learning
Hemant Rathore, Sanjay K. Sahay, Piyush Nikam +1
The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are suscept…
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS
Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
Recurrent deep learning language models like the LSTM are often used to provide advanced cyber-defense for high-value assets. The underlying assumption for using LSTM networks for…
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture
Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
The Intent in Android plays an important role in inter-process and intra-process communications. The implicit Intent that an application could accept are declared in its manifest a…
Android Malicious Application Classification Using Clustering
Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi +1
Android malware have been growing at an exponential pace and becomes a serious threat to mobile users. It appears that most of the anti-malware still relies on the signature-based…
Malware Detection using Machine Learning and Deep Learning
Hemant Rathore, Swati Agarwal, Sanjay K. Sahay +1
Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies ha…
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection
Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, n…
An investigation of a deep learning based malware detection system
Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Enco…