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Utkarsh Verma

Malware Evasion Attack and Defense

Apr 16, 2019
Yonghong Huang, Utkarsh Verma, Celeste Fralick, Gabriel Infante-Lopez, Brajesh Kumarz, Carl Woodward

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Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.

* Accepted by IEEE DSN-DSML2019

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On Optimizing Human-Machine Task Assignments

Sep 24, 2015
Andreas Veit, Michael Wilber, Rajan Vaish, Serge Belongie, James Davis, Vishal Anand, Anshu Aviral, Prithvijit Chakrabarty, Yash Chandak, Sidharth Chaturvedi, Chinmaya Devaraj, Ankit Dhall, Utkarsh Dwivedi, Sanket Gupte, Sharath N. Sridhar, Karthik Paga, Anuj Pahuja, Aditya Raisinghani, Ayush Sharma, Shweta Sharma, Darpana Sinha, Nisarg Thakkar, K. Bala Vignesh, Utkarsh Verma, Kanniganti Abhishek, Amod Agrawal, Arya Aishwarya, Aurgho Bhattacharjee, Sarveshwaran Dhanasekar, Venkata Karthik Gullapalli, Shuchita Gupta, Chandana G, Kinjal Jain, Simran Kapur, Meghana Kasula, Shashi Kumar, Parth Kundaliya, Utkarsh Mathur, Alankrit Mishra, Aayush Mudgal, Aditya Nadimpalli, Munakala Sree Nihit, Akanksha Periwal, Ayush Sagar, Ayush Shah, Vikas Sharma, Yashovardhan Sharma, Faizal Siddiqui, Virender Singh, Abhinav S., Anurag. D. Yadav

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When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machine accuracy is sub-optimal, and joint optimization of the combined system improves performance.

* HCOMP 2015 Work in Progress

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