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 :
- “What is Cybersecurity?” CISA, 1 February 2021, https://www.cisa.gov/news-events/news/what-cybersecurity. Accessed 4 November 2023.
- Meeuwisse, Raef. The Cybersecurity to English Dictionary: 4th Edition. Cyber Simplicity Limited, 2018.
- “Why Is Cybersecurity Important — Cybersecurity.” CompTIA, https://www.comptia.org/content/articles/why-is-cybersecurity- important. Accessed 4 November 2023.
- Steinberg, Joseph. Cybersecurity For Dummies. Wiley, 2022.
- “What is Cybersecurity? Defination, Importance and Types of Cyber- security.” EC-Council, https://www.eccouncil.org/what-is-cybersecurity/. Accessed 4 November 2023.
- Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. MIT Press, 2021.
- Stewart, Andrew, and Shostack. The New School of Information Secu- rity. Addison Wesley Professional, 2008.
- “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.
- “.”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.
- “intrusion - Glossary — CSRC.” NIST Computer Security Resource Center, https://csrc.nist.gov/ glossary/term/intrusion. Accessed 4 November 2023.
- ”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.
- “MACHINE LEARNING METHODS FOR MAL- WARE DETECTION AND CLASSIFICATION.” CORE, https://core.ac.uk/download/pdf/80994982. pdf. Accessed 4 November 2023.
- “What is a Remote Administration Tool (RAT)?” McAfee, https://www.mcafee.com/learn/what-is-rat/. Accessed 4 November 2023.
- Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer New York, 2006.
- Knox, Steven W. Machine Learning: A Concise Introduction. Wiley, 2018.
- “What is Supervised Learning?” IBM, https://www.ibm.com/topics/supervised-learning. Accessed 5 November 2023.
- “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.
- Chapelle, Olivier, et al., editors. Semi-supervised Learning. MIT Press, 2006.
- Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Edited by Richard S. Sutton, MIT Press, 1998.
- James, Gareth, et al. An Introduction to Statistical Learning: With Applications in R. Edited by Gareth James, Springer New York, 2013.
- “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.
- Steinberg, Joseph. Cybersecurity For Dummies. Wiley, 2019.
- “DETECTION OF MALWARE USING SVM.” IRJMETS, https://www.doi.org/10.56726/IRJMETS34910. Accessed 6 November 2023.
- Chumachenko, Kateryna. “Machine Learning Methods for Malware Detection and Classification.” (2017).
- 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.
- 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.
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.