A Study on Cyber Attacks Through Virtual Private Network (VPN) on Financial Institutions

Financial institutions continue to be high-value targets for increasingly sophisticated cyberattacks, with adversaries adopting advanced techniques to evade detection and exploit systemic vulnerabilities. Among these techniques, the misuse of Virtual Private Networks (VPNs) has emerged as a critical threat vector. VPNs allow attackers to conceal their identities, circumvent geo-location filters, and inject malicious traffic into secure environments while appearing legitimate (Zhang et al., 2018; ENISA, 2023). This study investigates the correlation between VPN-enabled network activity and cyberattack patterns within financial institutions, using a comprehensive dataset of 40,000 network traffic records. The dataset comprises 25 variables, including VPN flags, anomaly scores, intrusion detection alerts, malware signatures, attack types, user behaviour profiles, and device-level metadata. A quantitative, descriptive, and analytical research design was employed to examine how VPN-based connections influence the frequency, severity, and behavioural characteristics of cyberattacks. Statistical techniques and exploratory data analytics were utilized to identify anomalies, risk clusters, and significant deviations between VPN and non-VPN traffic. The analysis reveals that VPN-originated traffic exhibits distinctly higher anomaly scores, a greater concentration of IDS alerts, and stronger associations with malware-linked activities. Attack types such as phishing, brute-force attempts, unauthorized access, and data exfiltration showed heightened occurrence through VPN channels, indicating a deliberate strategy by adversaries to leverage the anonymity provided by VPN technologies. Furthermore, patterns observed in the dataset suggest that compromised or misconfigured VPN endpoints pose substantial vulnerabilities within financial networks. The findings emphasize the urgent need for financial institutions to strengthen their security frameworks—particularly concerning VPN access controls, multi-factor authentication enforcement, behavioral analytics, continuous threat monitoring, and zero-trust network models. By providing empirical, data-driven evidence of VPN-related risks, this study contributes valuable insights into the evolving cyber threat landscape in financial sectors. It highlights the importance of proactive defense mechanisms, advanced anomaly detection systems, and policy-level interventions to mitigate the growing challenge of VPN-enabled cyberattacks.