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The document presents a seminar outline on GRAPHMAL, a portable executable malware classifier. The seminar will introduce the objective of using a graph neural network and parameterized augmented API sequences to classify malware. It will provide background on GNNs, describe the dataset and features used, and is presented by Rajnee Kant under the supervision of Prof. Durga Prasad Mohapatra.
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Resume_1
Vishal K. Gupta is pursuing a Master of Science in Computer Science and Engineering at the University of Florida with a 4.0 GPA. He has a B.Tech in Electrical Engineering from Indian Institute of Technology Kanpur with a 7.0 GPA. He has over 3 years of professional experience as an Associate Engineer at Qualcomm India Pvt. Ltd. where he worked on software verification of security IPs and debugging firmware. He also has research experience working on fault detection, visual recognition, and condition monitoring of rotating machines using machine learning techniques. He is proficient in languages such as C/C++, Java, Python, and MATLAB.
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Master Slides_NITRKL.pptx
- 1. Presented by Author: Rajnee Kant Department of Computer Science & Engineering National Institute of Technology, Rourkela, Odisha - 769008 Contact: 222cs3383@nitrkl.ac.in GRAPHMAL: A Portable Executable Malware Classifier Based on GNN and Paramented Augmented API Sequences 11-08-2023 Under the Supervision of Prof. Durga Prasad Mohapatra Department of Computer Science & Engineering National Institute of Technology, Rourkela, Odisha - 769008 Contact: durga@nitrkl.ac.in 1
- 2. Seminar Outline Introduction Objective Motivation GNN (BackGround) DataSet Feature 11-08-2023 2
- 3. 26-09-2019 3 Arun Kumar Introduction The acronym MALWARE stands for malicious software, which poses serious security risks to computer systems. Viruses, worms, Trojan horses, spyware, adware, and other similar mechanisms fall under the broad definiti on of Malware.