Malware Detection by Visual Image Comparison between Four Different Hash Algorithms of Various Recognized Files

  • Authors

    • Shashi Kant Mishra Department of Computer Science & Engineering, Guru Nanak Institute of Technology, Hyderabad (Telangana)
    • Syed Asif Basha Department of Computer Engineering, College of Computer Science, King Khalid University, Abha-61421, K.S, A
    • Chiranjib Goswami Department of Electronics and Communication Engineering, Asansol Engineering College, Asansol, West Bengal- 713305, India
    • Chitra. M Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, Tamilnadu,India
    • Ajeet Kumar Srivastava Department of Electronics and Communication Engineering, School of Engineering and Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh-208024, India
    • M. R. Arun Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai – 600062, Tamilnadu, India
    • Bhaskar Roy Department of CSE(AIML), Asansol Engineering College, Asansol, West Bengal- 713305, India
    https://doi.org/10.14419/xx1ere40

    Received date: March 18, 2025

    Accepted date: May 1, 2025

    Published date: June 25, 2025

  • Antivirus labeling; Byteclass view; Dotplot visualization; Hash algorithms; Malware detection

  • Abstract

    Malware detection is a critical aspect of cybersecurity, and various techniques are employed to identify and combat malicious software. One approach involves using hash algorithms to generate unique identifiers for files, which can then be compared to known malware signatures. While hash algorithms are typically used for data integrity and digital signatures, there are specialized hash functions that can be applied to visual images. These algorithms, such as aHash, dHash, pHash, and wHash, offer unique capabilities for detecting malware through visual image analysis. The objective of this work is to perform malware detection by visual comparison between different Hash algorithms of vari-ous recognized malware, dubious files and clean files. It is expected that the identification of patterns will help in the faster detection of malicious code and even detect them when other mechanisms would not.

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  • How to Cite

    Mishra, S. K., Basha, S. A. ., Goswami, C. ., M , C., Srivastava , A. K., Arun, M. R. ., & Roy , B. . (2025). Malware Detection by Visual Image Comparison between Four Different Hash Algorithms of Various Recognized Files. International Journal of Basic and Applied Sciences, 14(2), 371-390. https://doi.org/10.14419/xx1ere40