Exploring the Role of Advanced Shell Scripts for Malware Threat Detection

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume :11 | Issue : 03 | Page : –
By
vector

P. Devi Sravanthi,

vector

Manas Kumar Yogi,

  1. Post Graduate Student, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_110705’);});Edit Abstract & Keyword

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: Malware, Shell Scripts, Threat, Cyber security, Behavioral Analysis

[This article belongs to Journal of Advances in Shell Programming (joasp)]

How to cite this article:
P. Devi Sravanthi, Manas Kumar Yogi. Exploring the Role of Advanced Shell Scripts for Malware Threat Detection. Journal of Advances in Shell Programming. 2024; 11(03):-.
How to cite this URL:
P. Devi Sravanthi, Manas Kumar Yogi. Exploring the Role of Advanced Shell Scripts for Malware Threat Detection. Journal of Advances in Shell Programming. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joasp/article=2024/view=0

Full Text PDF

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_110705’);});Edit

  1. Sudhakar, Kumar S. An emerging threat Fileless malware: a survey and research challenges. Cybersecurity. 2020 Jan 14;3(1):1.
  2. Alasmary H, Anwar A, Abusnaina A, Alabduljabbar A, Abuhamad M, Wang A, Nyang D, Awad A, Mohaisen D. SHELLCORE: Automating malicious IoT software detection using shell commands representation. IEEE Internet of Things Journal. 2021 Jun 3;9(4):2485-96.
  3. Saha A, Blasco J, Lindorfer M. Exploring the Malicious Document Threat Landscape: Towards a Systematic Approach to Detection and Analysis. In2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) 2024 Jul 8 (pp. 533-544). IEEE.
  4. Caviglione L, Choraś M, Corona I, Janicki A, Mazurczyk W, Pawlicki M, Wasielewska K. Tight arms race: Overview of current malware threats and trends in their detection. IEEE Access. 2020 Dec 30;9:5371-96.
  5. Barr-Smith F. Advances in detection and analysis of modern evasive malware (Doctoral dissertation, University of Oxford). 2023. Available from https://ora.ox.ac.uk/objects/uuid:0f15fe91-7b37-4dc9-964c-bf7efcf41caa
  6. Kara I. Fileless malware threats: Recent advances, analysis approach through memory forensics and research challenges. Expert Systems with Applications. 2023 Mar 15;214:119133.
  7. Poudyal S, Dasgupta D. AI-powered ransomware detection framework. In2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 Dec 1 (pp. 1154-1161). IEEE.
  8. Praveen MK. A Comparative Analysis of Malware Written in the C and Rust Programming Languages. Rochester Institute of Technology; 2023. 30493500.
  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 Sep 21;11(19):2992.
  10. Carrillo-Mondéjar J, Martínez JL, Suarez-Tangil G. Characterizing Linux-based malware: Findings and recent trends. Future Generation Computer Systems. 2020 Sep 1;110:267-81.
  11. Babar FM. Emerging & unconventional malware detection using a hybrid approach (Master’s thesis, University of Windsor (Canada)). 2020. 27737842. https://scholar.uwindsor.ca/cgi/viewcontent.cgi?article=9299&context=etd
  12. Nguyen HN, Abri F, Pham V, Chatterjee M, Namin AS, Dang T. MalView: Interactive visual analytics for comprehending malware behavior. IEEE Access. 2022 Sep 19;10:99909-30.
  13. Johansen MB. Development of a customized remote access trojan (RAT) for educational purposes within the field of malware analysis (Master’s thesis, NTNU). 2022. https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/3006997/no.ntnu%3Ainspera%3A106263136%3A64227736.pdf?sequence=1
  14. Wu MH, Hsu FH, Hunag JH, Wang K, Liu YY, Chen JX, Wang HJ, Yang HT. MPSD: A Robust Defense Mechanism against Malicious PowerShell Scripts in Windows Systems. Electronics. 2024 Sep 19;13(18):3717.
  15. Kawasoe R, Han C, Isawa R, Takahashi T, Takeuchi JI. Investigating behavioral differences between IoT malware via function call sequence graphs. InProceedings of the 36th Annual ACM Symposium on Applied Computing 2021 Mar 22 (pp. 1674-1682).
  16. Suwais K, Hnaif AA, Almanasra S. An Alternative Static Taint Analysis Framework to Detect PHP Web Shell-Based Web Attacks. International Journal of Advances in Soft Computing & Its Applications. 2023 Nov 1;15(3).

Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 22/10/2024
Accepted 29/10/2024
Published 04/11/2024

function myFunction2() {
var x = document.getElementById(“browsefigure”);
if (x.style.display === “block”) {
x.style.display = “none”;
}
else { x.style.display = “Block”; }
}
document.querySelector(“.prevBtn”).addEventListener(“click”, () => {
changeSlides(-1);
});
document.querySelector(“.nextBtn”).addEventListener(“click”, () => {
changeSlides(1);
});
var slideIndex = 1;
showSlides(slideIndex);
function changeSlides(n) {
showSlides((slideIndex += n));
}
function currentSlide(n) {
showSlides((slideIndex = n));
}
function showSlides(n) {
var i;
var slides = document.getElementsByClassName(“Slide”);
var dots = document.getElementsByClassName(“Navdot”);
if (n > slides.length) { slideIndex = 1; }
if (n (item.style.display = “none”));
Array.from(dots).forEach(
item => (item.className = item.className.replace(” selected”, “”))
);
slides[slideIndex – 1].style.display = “block”;
dots[slideIndex – 1].className += ” selected”;
}