Software plagiarism detection in multiprogramming languages using machine learning approach

Ullah, Farhan; Wang, Junfeng; Farhan, Muhammad; Habib, Masood; Khalid, Shehzad

doi:10.1002/cpe.5000

Cited by 21 publications

(17 citation statements)

References 27 publications

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“…Arguing that these metrics were not representative, the metrics was then extended by considering more factors [3,16] in which some of them are contextual (like source code tokens) [4,23]. The similarity measurement was also updated by adopting algorithms from other domains such as information retrieval [12,18,26], clustering [1,24,36], and classification [50,56]. Occasionally, algorithms from some of these domains were applied altogether [14,46].…”

Section: Related Workmentioning

confidence: 99%

“…Classification algorithms is also applicable for language-independent techniques in which one instance refers to a source code pair and its target class defines whether the pair is suspicious. Ullah et al [50] consider source code words as learning features and, therefore, raise the suspicion for some pairs with the help of multinomial logistic regression [6]. Yasaswi, Purini, and Jawahar [56] classify the suspicion through a character-level language model and a support-vector machine [10].…”

Section: Related Workmentioning

confidence: 99%

“…This can lead to an issue when plagiarism or collusion occurs; most existing detection techniques are not specifically designed to handle such cases in a cross-language manner where the involved source code files are written in different programming languages [2,21]. Some detection techniques have addressed the issue by considering the source code as raw text [7,50], removing any needs for language-specific components. Even though this kind of approach is applicable, it may lack effectiveness [43,44].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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Several computing courses allow students to choose which programming language they want to use for completing a programming task. This can lead to cross-language code plagiarism and collusion, in which the copied code file is rewritten in another programming language. In response to that, this paper proposes a detection technique which is able to accurately compare code files written in various programming languages, but with limited effort in accommodating such languages at development stage. The only language-dependent feature used in the technique is source code tokeniser and no code conversion is applied. The impact of coincidental similarity is reduced by applying a TF-IDF inspired weighting, in which rare matches are prioritised. Our evaluation shows that the technique outperforms common techniques in academia for handling language conversion disguises. Further, it is comparable to those techniques when dealing with conventional disguises.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Karnalim

2020

csci

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“…Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In the contribution by Ullah et al, “Software Plagiarism Detection in Multiprogramming Languages using Machine Learning Approach,” the authors proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches 8 . The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information.…”

Section: Themes Of This Special Issuementioning

confidence: 99%

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Jeon

Chehri

Cuomo

et al. 2019

Concurrency and Computation

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Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics.

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“…The software piracy is the development of software by reusing source codes illegally from someone else's work and disguise as the original version. The cracker may copy the logic of the original software by reverse engineering procedures and then design the same logic in another type of source codes [6]. It is a severe threat to internet security, which gives access to unlimited downloads of pirated software, open-source codes and, promotes and advertises of pirated versions.…”

Section: Introductionmentioning

confidence: 99%

Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

et al. 2019

Self Cite

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The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods. INDEX TERMS Internet of Things, data mining, cyber security, software piracy, malware detection.

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Software plagiarism detection in multiprogramming languages using machine learning approach

Cited by 21 publications

References 27 publications

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

TF-IDF Inspired Detection for Cross-Language Source Code Plagiarism and Collusion

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach

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