Integrating Business Intelligence with  AI-Driven Machine Learning for  Next-Generation Intrusion Detection Systems

Mohammad Majharul Islam Jabed, Ahmed Sohaib Khawer, Sharmin Ferdous, Danial Hadid Niton, Amit Banwari Gupta; Mohammed Shafeul Hossain

doi:10.15662/IJRAI.2023.0606007

Authors

Mohammad Majharul Islam Jabed, Ahmed Sohaib Khawer, Sharmin Ferdous, Danial Hadid Niton, Amit Banwari Gupta School of IT, Washington University of Science and Technology, USA Author
Mohammed Shafeul Hossain School of IT, Virginia University of Science & Technology, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606007

Keywords:

Intrusion Detection Systems (IDS), Business Intelligence (BI), Machine Learning (ML), Artificial Intelligence (AI), Cybersecurity Analytics

Abstract

Intrusion Detection Systems (IDS) remain essential for defending against increasingly complex cyberattacks. However, older rule-based and signature-based solutions often fail to respond to new threats in real time. In this paper, we suggest a hybrid model that combines Business Intelligence (BI) and Artificial Intelligence (AI)-based Machine Learning (ML) methods. This model aims to develop a next-generation IDS that can alert teams in advance and provide actionable information. The framework uses BI pipelines to consolidate heterogeneous network data. It then applies advanced ML models to identify anomalies and intrusions and feeds these findings back into BI dashboards. These dashboards support decision-making and strategic planning. We evaluate the model on benchmark intrusion datasets and a variety of ML algorithms. We measure accuracy in detection, false positives, and scalability. Results show that the BI+ML method is much more effective in detection. It also helps security teams comprehend attack patterns, assess the impact on resources, and prioritize mitigation. This paper presents a new architecture and roadmap for organizations seeking to evolve IDS from reactive systems into intelligence-driven, adaptive security platforms.

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