Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/569242
Title: A Cross Version Binary Code Similarity Detection Based on Deep Learning Approach
Researcher: Poornima, S
Guide(s): Mahalakshmi, R
Keywords: Binary Code Analysis
Binary Code Similarity Detection
Computer Science
Computer Science Artificial Intelligence
Cross-Architecture Binary Code
Cross Optimization
Deep Learning
Engineering and Technology
Malware Detection
Vulnerability Assessment
University: Presidency University, Karnataka
Completed Date: 2024
Abstract: According to a recent study, the prevalence of dangerous software, or malware, is increasing at an alarming rate. Some malware can hide itself in the system by using various obfuscation techniques. To safeguard computer systems and the Internet against malware, it must be identified before infecting a large number of computers. Recently, there have been numerous studies on malware detection technologies. Nevertheless, malware detection remains a hurdle. In contrast, Open Source Software (OSS) code is commonly reused in software development. However, reusing some specific OSS versions results in one-day vulnerabilities, the details of which are made public. These vulnerabilities could be abused and result in major- security problems. The most advanced OSS reuse detection methods now in use struggle to pinpoint the precise versions of OSS that are being reused. The matching scores are only based on resemblance, and the criteria they chose are not distinctive enough for version detection. Current techniques, such as behaviour-based, model checking-based, and deep learning-based approaches, work effectively for complex and unknown malware and can be used to detect some known and unknown malware. However, no approach can detect every piece of malware out there. This emphasizes how difficult it is to establish an efficient malware detection tool, as well as the potential for fresh research and approaches. Cross Version Binary Code Similarity Detector (SIMCODE-NET) is a well-optimized version Detection Method for Open Source Software (OSS) in commercial off-the-shelf (COTS) software, which is presented in this work. First, we go over five different types of version-specific programming elements that can be tracked in instances of both source and binary code. Based on the two levels of code features, we propose a two-stage version identification approach and classify these features into program-level and function-level features. In addition, SIMCODE-NET classifies various forms of OSS versions...
Pagination: 
URI: http://hdl.handle.net/10603/569242
Appears in Departments:School of Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File491.58 kBAdobe PDFView/Open
02_prelim pages.pdf457.88 kBAdobe PDFView/Open
03_content.pdf348.96 kBAdobe PDFView/Open
04_abstract.pdf145.6 kBAdobe PDFView/Open
05_chapter 1.pdf701.51 kBAdobe PDFView/Open
06_chapter 2.pdf844.06 kBAdobe PDFView/Open
07_chapter 3.pdf1.09 MBAdobe PDFView/Open
08_chapter 4.pdf1.79 MBAdobe PDFView/Open
09_chapter 5.pdf861.23 kBAdobe PDFView/Open
10_chapter 6.pdf296.27 kBAdobe PDFView/Open
11_annexures.pdf550.99 kBAdobe PDFView/Open
80_recommendation.pdf356.75 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: