Authors : Mudambi Geoffrey; Davis Matovu; Andrew Lukyamuzi; Semwogere Twaibu; Odongotoo Godfrey; Andrew Alunyu E.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/ymswx643

Scribd : https://tinyurl.com/mrx7ymej

DOI : https://doi.org/10.38124/ijisrt/26apr1852

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 rapid growth of wireless communication technologies has significantly increased the complexity and vulnerability of network environments, making them prime targets for sophisticated cyberattacks. Wireless network forensics plays a crucial role in investigating and responding to such incidents; however, existing forensic techniques often fall short in supporting effective and timely cybersecurity decision-making. These limitations include inadequate real-time analysis, poor scalability in handling large volumes of traffic data, limited automation, and insufficient integration with intelligent decision-support systems. This study aims to analyse existing wireless network forensic techniques and identify their limitations while proposing an intelligent traffic analysis framework to enhance cybersecurity decision-making. The research adopts a mixed-methods approach, combining qualitative analysis of current forensic tools and techniques with quantitative evaluation of network traffic datasets. The study further integrates machine learning algorithms and data analytics methods to develop models capable of detecting, classifying, and predicting anomalous network behaviour in wireless environments. The proposed framework emphasizes real-time traffic monitoring, intelligent anomaly detection, and automated forensic analysis to improve the accuracy, speed, and reliability of cyber incident investigations. Experimental results are expected to demonstrate that the integration of intelligent traffic analysis significantly enhances threat detection capabilities, reduces response time, and supports proactive and evidence-based cybersecurity decisions. This research contributes to the field of network forensics by bridging the gap between traditional forensic methods and intelligent analytical approaches. It provides a scalable and adaptive solution tailored to modern wireless network challenges and offers practical recommendations for improving forensic readiness and cybersecurity resilience in dynamic environments.

Keywords : Wireless Network Forensics; Intelligent Traffic Analysis; Cybersecurity Decision-Making; Machine Learning; Anomaly Detection; Network Security; Digital Forensics; Real-Time Monitoring; Intrusion Detection Systems; Data Analytics.

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