STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Advances in Shell Programming Review Article

Exploring the Role of Advanced Shell Scripts for Malware Threat Detection

  1. P. Devi Sravanthi Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem
  2. Manas Kumar Yogi Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem

Abstract

This study investigates the application of advanced shell scripts in detecting malware threats within computer systems. As cyber-attacks become more sophisticated, traditional detection methods frequently prove inadequate, highlighting the need for innovative approaches. The research highlights the effectiveness of shell scripting in automating the monitoring and analysis of system behavior, file integrity, and network traffic. By leveraging patterns and signatures of known malware, the scripts can identify anomalies indicative of malicious activities. The study also examines the use of machine learning techniques to improve detection accuracy and minimize false positives. The findings suggest that advanced shell scripts not only streamline the detection process but also empower system administrators with real-time insights into potential threats. Ultimately, this research contributes to the field of cyber security by providing a practical framework for utilizing shell scripts as a proactive defense mechanism against malware, fostering a more robust security posture in organizational environments.

Keywords

References (16)

  1. Sudhakar, Kumar S. An emerging threat Fileless malware: a survey and research challenges. Cybersecurity. 2020;3(1). doi:10.1186/s42400-019-0043-x
  2. Alasmary H, Anwar A, Abusnaina A, Alabduljabbar A, Abuhamad M, Wang A, et al. ShellCore: Automating Malicious IoT Software Detection Using Shell Commands Representation. IEEE Internet of Things Journal. 2022;9(4):2485-2496. doi:10.1109/jiot.2021.3086398
  3. Saha A, Blasco J, Lindorfer M. Exploring the Malicious Document Threat Landscape: Towards a Systematic Approach to Detection and Analysis. 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). 2024:533-544. doi:10.1109/eurospw61312.2024.00065
  4. Caviglione L, Choras M, Corona I, Janicki A, Mazurczyk W, Pawlicki M, et al. Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection. IEEE Access. 2021;9:5371-5396. doi:10.1109/access.2020.3048319
  5. Barr-Smith F. Advances in detection and analysis of modern evasive malware [Doctoral dissertation]. Oxford: University of Oxford; 2023.
  6. Kara I. Fileless malware threats: Recent advances, analysis approach through memory forensics and research challenges. Expert Systems with Applications. 2023;214:119133. doi:10.1016/j.eswa.2022.119133
  7. Poudyal S, Dasgupta D. AI-Powered Ransomware Detection Framework. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). 2020:1154-1161. doi:10.1109/ssci47803.2020.9308387
  8. Praveen MK. A Comparative Analysis of Malware Written in the C and Rust Programming Languages [Master’s thesis]. Rochester: Rochester Institute of Technology; 2023.
  9. Bhardwaj A, Kaushik K, Alomari A, Alsirhani A, Alshahrani MM, Bharany S. BTH: Behavior-Based Structured Threat Hunting Framework to Analyze and Detect Advanced Adversaries. Electronics. 2022;11(19):2992. doi:10.3390/electronics11192992
  10. Carrillo-Mondéjar J, Martínez JL, Suarez-Tangil G. Characterizing Linux-based malware: Findings and recent trends. Future Generation Computer Systems. 2020;110:267-281. doi:10.1016/j.future.2020.04.031
  11. Babar FM. Emerging & unconventional malware detection using a hybrid approach [Master's thesis]. Windsor: University of Windsor; 2020.
  12. Nguyen HN, Abri F, Pham V, Chatterjee M, Namin AS, Dang T. MalView: Interactive Visual Analytics for Comprehending Malware Behavior. IEEE Access. 2022;10:99909-99930. doi:10.1109/access.2022.3207782
  13. Johansen MB. Development of a customized remote access trojan (RAT) for educational purposes within the field of malware analysis [Master’s thesis]. Trondheim: Norwegian University of Science and Technology; 2022.
  14. Wu MH, Hsu FH, Hunag JH, Wang K, Liu YY, Chen JX, et al. MPSD: A Robust Defense Mechanism against Malicious PowerShell Scripts in Windows Systems. Electronics. 2024;13(18):3717. doi:10.3390/electronics13183717
  15. Kawasoe R, Han C, Isawa R, Takahashi T, Takeuchi J. Investigating behavioral differences between IoT malware via function call sequence graphs. Proceedings of the 36th Annual ACM Symposium on Applied Computing. 2021:1674-1682. doi:10.1145/3412841.3442041
  16. Suwais K, Hnaif AA, Almanasra S. An alternative static taint analysis framework to detect PHP web shell-based web attacks. Int J Adv Soft Compu Appl. 2023 Nov;15(3):117–31. doi:10.15849/IJASCA.231130.08.
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