1 code implementation • 31 Oct 2023 • Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen W. White, Sujay Kumar Jauhar
In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17. 5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks.
Ranked #3 on Visual Question Answering on ViP-Bench
no code implementations • 6 Jun 2022 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities.
no code implementations • 23 Sep 2021 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
Long-Short-Term-Memory (LSTM) networks have shown great promise in artificial intelligence (AI) based language modeling.
no code implementations • 23 Sep 2021 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
The so generated data and simulations could be used to bolster the defenses of an IDS against an actual AI-based metamorphic attack from advanced malware and ransomware.
no code implementations • 12 Sep 2021 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
We also developed a system named DRoID that uses the DRo mechanism for enhancing the performance of an existing Malware Detection System that uses (low information features like the) Android implicit Intent(s) as the only features.
no code implementations • 28 Feb 2021 • Hemant Rathore, Sanjay K. Sahay, Shivin Thukral, Mohit Sewak
Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries.
no code implementations • 28 Feb 2021 • Hemant Rathore, Sanjay K. Sahay, Ritvik Rajvanshi, Mohit Sewak
In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network.
no code implementations • 1 Feb 2021 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware.
no code implementations • 27 Jan 2021 • Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak
Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15. 22% against a single policy attack, thereby increasing the robustness of the detection models i. e. the proposed model can effectively detect variants (metamorphic) of malware.
no code implementations • 26 Dec 2020 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
In the process, we also determine the relative importance of all the different hyper-parameters of an LSTM network as applied to malware detection using their op-code sequence representations.
no code implementations • 16 Oct 2020 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS.
no code implementations • 16 Oct 2020 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
So far neither the feasibility of developing an Intrusion Detection System solely on implicit Intent has been explored, nor are any benchmarks available of a malware classifier that is based on implicit Intent alone.
no code implementations • 21 Apr 2019 • Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi, Mohit Sewak
However, it appears that detection accuracy can be improved by using the clustering method.
no code implementations • 4 Apr 2019 • Hemant Rathore, Swati Agarwal, Sanjay K. Sahay, Mohit Sewak
Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection.
no code implementations • 16 Sep 2018 • 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, neural machine translation, etc.
no code implementations • 16 Sep 2018 • Mohit Sewak, Sanjay K. Sahay, Hemant Rathore
We investigate a Deep Learning based system for malware detection.