Authors : Akshat Kotadia; Bhavy Masalia; Om Mehra; Lakshin Pathak

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/mryvvsx8

Scribd : https://tinyurl.com/25nf3c

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN655

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The paper examines the application of machine learning (ML) techniques in the field of cybersecurity with the aim of enhancing threat detection and response capabilities. The initial section of the article provides a comprehensive examination of cybersecurity, highlighting the increasing significance of proactive defensive strategies in response to evolving cyber threats. Subsequently, a comprehensive overview of prevalentonline hazards is presented, emphasizing the imperative for the development of more sophisticated methodologies to detect and mitigate such risks. The primary emphasis of this work is to the practical use of machine learning in the identification and detection of potential dangers inside real-world contexts. This study examines three distinct cases: the detection of malware, attempts to breach security, and anomalous behavior shown by software. Each case study provides a detailed breakdown of the machine learning algorithms and approaches employed, demonstrating their effectiveness in identifying and mitigating risks. The paper further discusses the advantages and disadvantages associated with employing machine learning techniques for threat detection. One advantage of this approach is its ability to facilitatethe examination of extensive datasets, identification of intricate patterns, and prompt decision-making. However, discussions also revolve around difficulties like as erroneous discoveries, adversarial attacks, and concerns over privacy.

Keywords : Cybersecurity, Threat Detection, Machine Learning, Malware Detection, Intrusion Detection, Anomalous Behavior, Cyber Threats, Security Measures, Risk Mitigation, Cybersecurity Challenges, Threat Identification, Response Capabilities, Software Security, Network Security.

References :

1. “What is Cybersecurity?” CISA, 1 February 2021, https://www.cisa.gov/news-events/news/what-cybersecurity. Accessed 4 November 2023.
2. Meeuwisse, Raef. The Cybersecurity to English Dictionary: 4th Edition. Cyber Simplicity Limited, 2018.
3.  “Why Is Cybersecurity Important — Cybersecurity.” CompTIA, https://www.comptia.org/content/articles/why-is-cybersecurity- important. Accessed 4 November 2023.
4. Steinberg, Joseph. Cybersecurity For Dummies. Wiley, 2022.
5.  “What is Cybersecurity? Defination, Importance and Types of Cyber- security.” EC-Council, https://www.eccouncil.org/what-is-cybersecurity/. Accessed 4 November 2023.
6. Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2021.
7. Stewart, Andrew, and Shostack. The New School of Information Secu- rity. Addison Wesley Professional, 2008.
8.  “What is Cyber Security? — Definition, Types, and User Protection.” Kaspersky, https://www.kaspersky.com/resource-center/ definitions/what- is-cyber-security. Accessed 4 November 2023.
9.  “.”YouTube, 2 October 2022, This behavior would be considered abnormal as it diverges https://www.ic3.gov/Media/PDF/AnnualReport/2022_IC3Report.p. Accessed 4 November 2023.
10.  “intrusion - Glossary — CSRC.” NIST Computer Security Resource Center, https://csrc.nist.gov/ glossary/term/intrusion. Accessed 4 November 2023.
11. ”Malware Detection and Defense,” Research Gate, 2 October 2022, escalation in network traffic directed towards a specific server. https://www.researchgate.net/publication/368563807_Malware_4 November 2023.
12.  “MACHINE LEARNING METHODS FOR MAL- WARE            DETECTION AND CLASSIFICATION.” CORE, https://core.ac.uk/download/pdf/80994982. pdf. Accessed 4 November 2023.
13.  “What is a Remote Administration Tool (RAT)?” McAfee, https://www.mcafee.com/learn/what-is-rat/. Accessed 4 November 2023.
14. Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer New York, 2006.
15. Knox, Steven W. Machine Learning: A Concise Introduction. Wiley, 2018.
16.  “What is Supervised Learning?” IBM, https://www.ibm.com/topics/supervised-learning. Accessed 5 November 2023.
17.  “What Is Unsupervised Learning? Definition and Examples.” In- deed, 8 August 2022, https://www.indeed.com/career-advice/career- development/unsupervised-learning. Accessed 5 November 2023.
18. Chapelle, Olivier, et al., editors. Semi-supervised Learning. MIT Press, 2006.
19. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Edited by Richard S. Sutton, MIT Press, 1998.
20. James, Gareth, et al. An Introduction  to Statistical Learning: With Applications in R. Edited by Gareth James, Springer New York, 2013.
21.  “What are Intrusion Attempts and Their Impact on Businesses?” Secure Network Solutions, 13 October 2023, https://www.snsin.com/what-are- intrusion-attempts-their-impact-on-businesses/. Accessed 6 November 2023.
22. Steinberg, Joseph. Cybersecurity For Dummies. Wiley, 2019.
23.  “DETECTION OF MALWARE USING SVM.” IRJMETS, https://www.doi.org/10.56726/IRJMETS34910. Accessed 6 November 2023.
24. Chumachenko, Kateryna. “Machine Learning Methods for Malware Detection and Classification.” (2017).
25. Bokolo, Biodoumoye, Razaq Jinad, and Qingzhong Liu. ”A Comparison Study to Detect Malware using Deep Learning and Machine learning Techniques.” 2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI). IEEE, 2023.
26. J. A. Abraham and V. R. Bindu, ”Intrusion Detection and Pre- vention in Networks Using Machine   Learning and Deep   Learn- ing Approaches: A Review,” 2021 International Conference on Ad- vancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2021, pp. 1-4, doi: 10.1109/ICAECA52838.2021.9675595.