Abstract: This paper presents an extensive case study on the wireless security framework of READ Corporation, a multinational technology leader. We evaluate existing security measures, identify critical vulnerabilities, and analyze historical incidents using a synthetic dataset of 1000 security incidents. Our methodology integrates exploratory data analysis (EDA), predictive modeling with a Random Forest Classifier, detailed incident analysis, cost-benefit analysis, and case study comparisons. Findings reveal vulnerabilities such as ’Evil Twin attacks, insider threats, malware infections, and IoT-related risks, with predictive models achieving a limited 28% accuracy due to dataset constraints. We propose upgrading to WPA3, implementing two-factor authentication (2FA), enhancing employee training, securing physical access points, deploying AI-driven intrusion detection systems (IDS), and managing IoT devices. A cost-benefit analysis projects $2.925M in savings against $1.95M in costs. This study emphasizes the need for continuous security enhancements, offering a scalable framework for organizations to bolster wireless security resilience.

Keywords: Wireless Security, READ Corporation, WPA2, WPA3, Random Forest Classifier, Evil Twin Attacks, Insider Threats, Two-Factor Authentication, Intrusion Detection Systems, IoT Security

Pages: 70-78

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