abstract: Due to the increase in computer and database dependency, the damage caused by malicious codes increases. Moreover, gravity and the magnitude of malicious attacks by hackers grow at an unprecedented rate. A key challenge lies on detecting such malicious attacks and codes in real-time by the use of existing methods, such as a signature-based detection approach. To this end, computer scientists have attempted to classify heterogeneous types of malware on the basis of their observable characteristics. Existing literature focuses on classifying binary codes, due to the greater accessibility of malware binary than source code. Also, for the improved speed and scalability, machine learning-based approaches are widely used. Despite such merits, the machine learning-based approach critically lacks the interpretability of its outcome, thus restricts understandings of why a given code belongs to a particular type of malicious malware and, importantly, why some portions of a code are reused very often by hackers. In this light, this study aims to enhance understanding of malware by directly investigating reused codes and uncovering their characteristics.
To examine reused codes in malware, both malware with source code and malware with binary code are considered in this thesis. For malware with source code, reused code chunks in the Mirai botnet. This study lists frequently reused code chunks and analyzes the characteristics and location of the code. For malware with binary code, this study performs reverse engineering on the binary code for human readers to comprehend, visually inspects reused codes in binary ransomware code, and illustrates the functionality of the reused codes on the basis of similar behaviors and tactics.
This study makes a novel contribution to the literature by directly investigating the characteristics of reused code in malware. The findings of the study can help cybersecurity practitioners and scholars increase the performance of malware classification. / Dissertation/Thesis / Masters Thesis Computer Science 2020
| Identifer | oai:union.ndltd.org:asu.edu/item:62686 |
| Date | January 2020 |
| Contributors | LEe, Yeonjung (Author), Bao, Youzhi (Advisor), Doupé, Adam (Committee member), Shoshitaishvili, Yan (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 42 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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