Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion

Author:

Vasani Vatsal1ORCID,Bairwa Amit Kumar1ORCID,Joshi Sandeep1,Pljonkin Anton2ORCID,Kaur Manjit3ORCID,Amoon Mohammed4ORCID

Affiliation:

1. Manipal University Jaipur, Jaipur-Ajmer Express Highway, Dehmi Kalan, Jaipur 303007, India

2. Institute of Computer Technologies and Information Security, Southern Federal University, Bol’shaya Sadovaya Ulitsa, 105/42, 344006 Rostov-on-Don, Russia

3. School of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India

4. Department of Computer Science, Community College, King Saud University, P. O. Box 28095, Riyadh 11437, Saudi Arabia

Abstract

In this paper, we explore how incident handling procedures are currently being implemented to efficiently mitigate malicious software. Additionally, it aims to provide a contextual understanding of diverse malcodes and their operational processes. This study also compares various ways of detecting adware against a selection of anti-virus software. Moreover, this paper meticulously examines the evolution of hacking, covering the methods employed and the actors involved. A comparative analysis of three prominent malware detection tools, Google Rapid Response (GRR), Wireshark, and VirusTotal, is also conducted, aiding in informed decision-making for enhancing application security. This paper reaches its conclusion by conducting an exhaustive analysis of two case studies, offering valuable insights into a diverse range of potential leaks and virus attacks that may pose threats to various conglomerates. In essence, this article provides a comprehensive overview that spans incident handling procedures, the historical development of hacking, and the diverse spectrum of tools accessible for achieving effective malware detection.

Funder

King Saud University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference76 articles.

1. Lopez, J., and Hammerli, B. (2007, January 3–5). A Malware Detector Placement Game for 954 Intrusion Detection. Proceedings of the 2nd International Workshop on Critical Information Infrastructures Security, Malaga, Spain.

2. Mathematical Modelling of Malware Intrusion in Computer Networks;Lazarov;Cybern. Inf. Technol.,2022

3. A proactive approach to intrusion detection and malware collection;Chen;Secur. Commun. Netw.,2013

4. Arabnia, H., Aissi, S., and Mun, Y. (2004, January 21–24). Malware mitigation using host intrusion prevention in the enterprise. Proceedings of the International Conference on Security and Management, Las Vegas, NV, USA.

5. Kidmose, E., Stevanovic, M., and Pedersen, J.M. (2016, January 13–14). Correlating intrusion detection alerts on bot malware infections using neural network. Proceedings of the 2016 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), London, UK.

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