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Relevant bibliographies by topics / Obfuscated computer virus

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Journal articles

Academic literature on the topic 'Obfuscated computer virus'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Obfuscated computer virus"

1

Tan, Hui Xin, Ismail Ismahani, and Mohammed Khammas Ban. "Obfuscated computer virus detection using machine learning algorithm." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1383–91. https://doi.org/10.11591/eei.v8i4.1584.

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Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the t

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2

Li, Qi Lin, Zong Qu Zhao, and Jun Feng Wang. "Research of Computer Virus Detection Technology Based on the Virtualization Platform." Applied Mechanics and Materials 441 (December 2013): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amm.441.405.

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The characteristics of computer virus runtime behavior can be found by dynamic monitoring technology which deals with the obfuscated or packed computer virus. Virtual technology can provides safety monitoring environment for software, therefore the especially virtual environment is scarce to design for virus monitoring analysis. In this paper, a virtual computer virus monitoring is introduced based on the secondary development of Xen. The scheme can provide a more comprehensive and security computer virus monitoring on the basis of the virtual hardware.

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3

Tang, Ke, Zheng Shan, Chunyan Zhang, Lianqiu Xu, Meng Qiao, and Fudong Liu. "DFSGraph: Data Flow Semantic Model for Intermediate Representation Programs Based on Graph Network." Electronics 11, no. 19 (2022): 3230. http://dx.doi.org/10.3390/electronics11193230.

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With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-

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4

Gunawan, Indra. "Analisis Malware Botnet Proteus Pendekatan Static dan Dinamic." SIMETRIS 15, no. 1 (2021): 12–17. http://dx.doi.org/10.51901/simetris.v15i01.172.

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Botnet Proteus dianggap sebagai salah satu botnet yang mengerikan dampaknya terhadap dunia internet di kisaran tahun 2017, botnet ini mulai dideteksi akhir tahun 2016 dan terus menyebar setelahnya. Proteus adalah botnet yang sample virusnya sulit didapatkan sehingga sangat sedikit penelitian yang membahasnya. Dalam penelitian ini digunakan pendekatan static dan dinamis untuk proses analisis terhadap botnet, pendekatan static dilakukan dengan analisis file virus secara langsung, sedangkan pada pendekatan dynamic, analisa dilakukan melalui jaringan terhadap paket-paket. Hasil analisis menunjukka

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5

Ndibanje, Bruce, Ki Kim, Young Kang, Hyun Kim, Tae Kim, and Hoon Lee. "Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction." Applied Sciences 9, no. 2 (2019): 239. http://dx.doi.org/10.3390/app9020239.

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Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate

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6

Xin, Tan Hui, Ismahani Ismail, and Ban Mohammed Khammas. "Obfuscated computer virus detection using machine learning algorithm." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019). http://dx.doi.org/10.11591/eei.v8i4.1584.

Full text

Abstract:

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the t

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7

Mimura, Mamoru, and Ryo Ito. "Applying NLP techniques to malware detection in a practical environment." International Journal of Information Security, June 6, 2021. http://dx.doi.org/10.1007/s10207-021-00553-8.

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AbstractExecutable files still remain popular to compromise the endpoint computers. These executable files are often obfuscated to avoid anti-virus programs. To examine all suspicious files from the Internet, dynamic analysis requires too much time. Therefore, a fast filtering method is required. With the recent development of natural language processing (NLP) techniques, printable strings became more effective to detect malware. The combination of the printable strings and NLP techniques can be used as a filtering method. In this paper, we apply NLP techniques to malware detection. This paper

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