[{“box”:0,”content”:”
n
n
- n t
n
Manas Kumar Yogi, Yamuna Mundru
[/foreach]
n
n
n
- [foreach 286] [if 1175 not_equal=””]n t
- Assistant Professor, Assistant Professor, Department of computer science and engineering, Pragati Engineering College (Autonomous), ,CSE-AI& ML Department, Pragati Engineering College (Autonomous), Andhra Pradesh, Andhra Pradesh, India, India
n[/if 1175][/foreach]
n
Abstract
nThe ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This paper explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio- inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The paper begins by introducing the GWO algorithm, elucidating its fundamental principles and mechanisms. It subsequently details how GWO can be effectively adapted to the realm of malware analysis, emphasizing its role in improving the selection of discriminative features and optimizing the parameters of machine learning models. In pursuit of empirical validation, a comprehensive experimental setup is presented, featuring diverse malware datasets, well-defined evaluation metrics, and baseline models for performance comparison. The experimental results unveil compelling findings: the deployment of GWO consistently yields substantial enhancements in the accuracy and resilience of malware detection systems. Notably, GWO exhibits remarkable effectiveness in addressing the dynamic and polymorphic nature of contemporary malware, making it a valuable asset for real-time threat identification. The significance of these discoveries has broad implications for both research and practical use in the realm of cybersecurity. The deployment of GWO emerges as a potent strategy for fortifying the capabilities of malware detection systems, rendering them more adaptive and proficient in discerning emerging threats. Furthermore, this paper underscores the importance of exploring innovative, nature-inspired approaches, such as GWO, to keep pace with the ever-shifting landscape of cyber threats. In conclusion, this paper illuminates the promising potential of the Grey Wolf Optimizer (GWO) mechanism as a transformative tool for malware analysis, ushering in a new era of precision and efficiency in the on-going battle against malware-induced vulnerabilities.
n
Keywords: Grey Wolf Optimizer, Malware, Virus, Trojan, Threat, Security
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Wireless Security and Networks(ijwsn)]
n
n
n
n
n
nn
nn
Full Text
n[if 992 equals=”Open Access”] [else] nvar fieldValue = “[user_role]”;nif (fieldValue == ‘indexingbodies’) {n document.write(”);n }nelse if (fieldValue == ‘administrator’) { document.write(”); }nelse if (fieldValue == ‘ijwsn’) { document.write(”); }n else { document.write(‘ ‘); }n [/if 992] [if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
[1] Or-Meir, Ori, et al.Dynamic malware analysis in the modern era—A state of the art
survey. ACM Computing Surveys (CSUR) 52.5 (2019): 1-48.
[2] Egele, Manuel, et al. A survey on automated dynamic malware-analysis techniques and
tools. ACM computing surveys (CSUR) 44.2 (2008): 1-42.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
n
n
n
n
| Volume | 01 |
| Issue | 02 |
| Received | September 11, 2023 |
| Accepted | September 22, 2023 |
| Published | September 25, 2023 |
n
n
n
n
n[/foreach]
n[/if 1190] [if 1177 not_equal=””]n
n
n[/foreach]
n[/if 1177]
n
n
n
n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n n function myfun() {n x = document.getElementById(“editor”);n y = document.getElementById(“down”);n z = document.getElementById(“up”);n if (x.style.display == “none”) {n x.style.display = “block”;n }n else {n x.style.display = “none”;n }n if (y.style.display == “none”) {n y.style.display = “block”;n }n else {n y.style.display = “none”;n }n if (z.style.display == “none”) {n z.style.display = “block”;n }n else {n z.style.display = “none”;n }n }n function myfun2() {n x = document.getElementById(“reviewer”);n y = document.getElementById(“down2”);n z = document.getElementById(“up2”);n if (x.style.display == “none”) {n x.style.display = “block”;n }n else {n x.style.display = “none”;n }n if (y.style.display == “none”) {n y.style.display = “block”;n }n else {n y.style.display = “none”;n }n if (z.style.display == “none”) {n z.style.display = “block”;n }n else {n z.style.display = “none”;n }n }n”}]