OBA2: An Onion approach to Binary code Authorship Attribution
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چکیده
منابع مشابه
OBA2: An Onion approach to Binary code Authorship Attribution
A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer’s skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attr...
متن کاملCorrigendum to 'OBA2: An Onion approach to Binary code Authorship Attribution' [Digit Investig 11 (2014) S94-S103]
The authors state that, Algorithms 1 and 2 (on page 5), together with their explanations, were not correctly cited in the original article. The Algorithms are borrowed from the authors previously published work (which is a Master thesis co-supervised by Dr. Mourad Debbabi and Dr. Benjamin Fung). The correct citation for Algorithms 1 and 2 is listed below; Farhadi, MR. Assembly Code Clone Detect...
متن کاملPoster: Source Code Authorship Attribution
As information becomes widely available and easily accessible through the Internet and other sources, the trend of plagiarism has been increasing. Plagiarism and copyright infringement are issues that come up in both academic and corporate environments. We need author classification techniques to inhibit such unethical violations. Source code is also intellectual property and reflects individua...
متن کاملSource Code Authorship Attribution using n-grams
Plagiarism and copyright infringement are major problems in academic and corporate environments. Existing solutions for detecting infringements in structured text such as source code are restricted to textual similarity comparisons of two pieces of work. In this paper, we examine authorship attribution as a means for tackling plagiarism detection. Given several samples of work from several auth...
متن کاملAn Off-the-shelf Approach to Authorship Attribution
Authorship detection is a challenging task due to many design choices the user has to decide on. The performance highly depends on the right set of features, the amount of data, in-sample vs. out-of-sample settings, and profilevs. instance-based approaches. So far, the variety of combinations renders off-the-shelf methods for authorship detection inappropriate. We propose a novel and generally ...
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ژورنال
عنوان ژورنال: Digital Investigation
سال: 2014
ISSN: 1742-2876
DOI: 10.1016/j.diin.2014.03.012