Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

Zhou

Concurrency and Computation

et al. 2018

Summary Regarding the current situation that the recognition rate of malware is decreasing, the article points out that the reason for this dilemma is that more and more targeting malware have emerged, which share little or no common feature with traditional malware. The premise of malware recognition judging whether a software is malicious or benign is actually a decision problem. We propose that malware discrimination should resort to the corresponding task or purpose. We first present a formal definition of a task and then provide further classifications of malicious tasks. Based on the decidable theory, we prove that task performed by any software is recursive and determinable. By establishing a mapping from software to task, we prove that software is many‐to‐one reducible to corresponding tasks. Thus, we demonstrate that software, including malware, is also recursive and can be determined by the corresponding tasks. Finally, we present the discrimination process of our method. Nine real malwares are presented, which were firstly discriminated by our method but at that time could not be identified by Kaspersky, McAfee, Symantec Norton, or Kingsoft Antivirus.

Section: Related Workmentioning

confidence: 99%

Targeting malware discrimination based on reversed association task

Zhou

Concurrency and Computation

et al. 2018

“…Their approaches are not suitable for programs that invoke few system calls. The latest dynamic birthmark work, DYKIS [29], measures the similarity of key instructions (e.g., value-updating instructions and inputcorrelated instructions in the execution traces, achieving good resilience to obfuscation schemes.…”

Section: A Software Plagiarism Detectionmentioning

confidence: 99%

Deviation-Based Obfuscation-Resilient Program Equivalence Checking With Application to Software Plagiarism Detection

Jiang

Zhang

et al. 2016

IEEE Trans. Rel.

Software plagiarism, an act of illegally copying others' code, has become a serious concern for honest software companies and the open source community. Considerable research efforts have been dedicated to searching the evidence of software plagiarism. In this paper, we continue this line of research and propose LoPD, a deviation-based program equivalence checking approach, which is an ideal fit for the whole-program plagiarism detection. Instead of directly comparing the similarity between two programs, LoPD searches for any dissimilarity between two programs by finding an input that will cause these two programs to behave differently, either with different output states or with semantically different execution paths. As long as we can find one dissimilarity, the programs are semantically different; but if we cannot find any dissimilarity, it is more likely a plagiarism case. We leverage dynamic symbolic execution to capture the semantics of execution paths and to find path deviations. Compared to the existing detection approaches, LoPD's formal program semantics-based method is more resilient to automatic obfuscation schemes. Our evaluation results indicate that LoPD is effective in detecting whole-program plagiarism. Furthermore, we demonstrate that LoPD can be applied to partial software plagiarism detection as well. The encouraging experiment results show that LoPD is an appealing complement to existing software plagiarism detection approaches.Index Terms-Path deviation, program logic, semantical difference, software plagiarism, symbolic execution.

Higher Education Pedagogies

“…There have been several techniques for detecting plagiarism, which mainly use software tools for detecting the different types of plagiarism (Cosma & Joy 2012;Halak & El-Hajjar, 2016;Jhi et al, 2015;Jiffriya, Jahan, Ragel, & Deegalla, 2013;Li, Chen, Xin, Bin, & Vitanyi, 2004;Rosales et al, 2008a;Tian et al, 2015). These techniques are mostly based on comparing text and finding any textual similarity to published material available in the software repositories, which is then validated by an instructor (Li et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of plagiarism prevention and detection techniques in engineering education

Halak

El‐Hajjar

2019

Higher education students are expected to develop critical analysis and creative thinking skills, where plagiarism can damage the development of these skills in addition to damaging the whole education process and experience. Furthermore, plagiarism undermines the trust between the lecturers and students and the reputation of the academic institutions can be affected if plagiarism is not considered seriously, where the degrees offered by these institutions can be devalued. In this paper, two plagiarism prevention techniques followed by two plagiarism detection techniques used in the engineering education in the University of Southampton are presented. The plagiarism prevention techniques presented are based on assigning individual coursework specifications to students and the use of individual presentation of coursework findings. Then, the plagiarism detection techniques are based on detecting the writing styles of students and testing the student' codes in different configurations.