Enhancing Cloud Information Security using Honey Badger Algorithm
Lakshmi Banavath, N Radhika Amareshwari, S Reddy, Rampaka Manoj Kumar, C. Revathi, Santhosh Kumar Medishetti
Abstract
In cloud computing environments, data leakage poses a severe threat to information security, often resulting from insider attacks, misconfigurations, or compromised credentials. To address this challenge, this study proposes the Honey Badger Algorithm (HBA), a novel, bio-inspired approach for data leakage detection and prevention in cloud systems. Mimicking the adaptive and fearless behavior of the honey badger, HBA combines behavior-based anomaly detection with intelligent decoy (honeypot) deployment to identify and mislead malicious users. The algorithm continuously monitors access patterns, flags suspicious deviations, and dynamically inserts decoy data to trap intruders, thereby preventing real data compromise. Extensive simulations conducted using a cloud-based testbed demonstrate that HBA achieves a 28.6% improvement in detection accuracy, a 31.4% reduction in false positives, and a 24.7% faster response time compared to traditional machine learning-based and rule-based methods. Furthermore, the lightweight nature of the algorithm ensures only a 12.3% increase in computational overhead, making it suitable for real-time, scalable cloud applications. The results confirm that HBA not only enhances proactive security but also strengthens data confidentiality and system trustworthiness. This research establishes HBA as a robust and intelligent mechanism to mitigate information leakage risks in modern cloud infrastructures.