Research Article

Leveraging Advanced Machine Learning Algorithms for Enhanced Cyberattack Detection on U.S. Business Networks

Authors

  • Md Rasibul Islam Department of Management Science and Quantitative Methods, Gannon University, USA
  • Md Nasiruddin Department of Management Science and Quantitative Methods, Gannon University, USA
  • Mitu Karmakar School of Business, International American University, Los Angeles, California, USA
  • Rabeya Akter Master of science in information technology. Washington University of Science and Technology, USA
  • MD Tushar Khan Master of Science in Business Analytics, Trine University, USA
  • Abdullah AL Sayeed Master of Business Administration in Project Management, Central Michigan University, USA
  • Al Amin Accounting and Information Systems, Jahangirnagar University, Dhaka, Bangladesh

Abstract

Cyberattacks' rising volume and sophistication have made conventional security measures, such as firewalls, signature-based intrusion detection systems, and antivirus software, increasingly inadequate. The upsurge of cyber threats has been one of the most pressing predicaments for U.S. organizations in the digital age. With the increased dependence on internet-based forums, cloud computing, and interconnected networks, companies face an advancing number of extreme cyberattacks. The chief objective of this research project is to design and deploy proven machine learning methods to enhance the detection and combating of cyberattacks on U.S. organization networks. This research project retrieved a cyber-attack dataset from Kaggle.com, which had a collection of public datasets of cyber threats. This dataset was curated precisely, offering a realistic representation of cyber-attack scenarios, making it an ideal playground for various analytical tasks. The collection was classified as per the source of the relevant information, such as host-based datasets, network traffic datasets, malware or fraud reports, or a special section for datasets that can be classified according to a specific source. The dataset comprised numerous network traffic attributes such as source and destination IP addresses, ports, protocol, payload size, and attack labels. For this research project, three machine learning algorithms were used, namely: Logistic Regression, XG-Boost and Random Forest. This research project applied performance metrics such as accuracy, precision, recall, and F1 score for the performance of the classification models were considered. The result illustrated that the random forest model was far superior in accuracy compared to the logistic regression model; particularly, it had excellent accuracy. Through the use of advanced machine learning models, organizations will be in a position to devise more dynamic and intelligent security systems that evolve with the threat landscape. These intelligent systems monitor every kind of anomaly, malicious activity, and threat response with unparalleled effectiveness. The findings of this research project have significant implications for enhancing cybersecurity in U.S. organizational networks.

Article information

Journal

Journal of Business and Management Studies

Volume (Issue)

6 (5)

Pages

213-224

Published

2024-10-26

How to Cite

Leveraging Advanced Machine Learning Algorithms for Enhanced Cyberattack Detection on U.S. Business Networks. (2024). Journal of Business and Management Studies, 6(5), 213-224. https://doi.org/10.32996/jbms.2024.6.5.23

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Keywords:

Cybersecurity, Machine Learning, Cyberattack Detection, U.S. Business Networks, Advanced Algorithms

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