Security and Privacy in Android: Emerging Threats and Gemini AI-Powered Detection Framework

Naga Satya Praveen Kumar Yadati; Diwakar Reddy Peddinti; Saurabh Prakash Shetty

doi:10.3991/ijim.v20i17.62514

Authors

Naga Satya Praveen Kumar Yadati Independent Researcher, San Jose, CA, USA https://orcid.org/0009-0004-6559-3158
Diwakar Reddy Peddinti Independent Software Engineer, USA https://orcid.org/0009-0005-0854-1690
Saurabh Prakash Shetty Independent Senior Mobile Software Engineer, USA https://orcid.org/0009-0005-0472-4174

DOI:

https://doi.org/10.3991/ijim.v20i17.62514

Keywords:

Adaptive Security Mechanisms, Adaptive AI, Android-based mobile learning

Abstract

Android remains the world’s dominant mobile operating system with over 3.5 billion active devices, making it a prime target for increasingly sophisticated security threats and privacy violations. Traditional signature-based and rule-driven defenses are proving insufficient against polymorphic malware, zero-day exploits, and context-aware privacy attacks. This paper presents a comprehensive investigation into security vulnerabilities and privacy challenges in the Android ecosystem, with a particular focus on how Google’s latest Gemini AI family, including Gemini Nano for on-device inference, Gemini Pro for cloud-assisted analysis, and Gemini 1.5 for long-context threat intelligence, fundamentally transforms threat detection and privacy enforcement. We survey the contemporary Android threat landscape spanning ransomware, banking trojans, spyware, and supply chain attacks, then systematically evaluate Gemini AI’s contributions to behavioral malware detection, real-time permission auditing, contextual privacy advisory, and natural language-driven threat intelligence. We further propose GeminiShield, a layered security architecture that integrates Gemini AI across the Android software stack, and validate its efficacy through comparative analysis against state-of-the-art approaches. Our results demonstrate detection accuracy of 97.3%, outperforming prior approaches while maintaining acceptable computational overhead on resource-constrained devices. Open challenges including adversarial attacks on AI models, on-device inference constraints, and regulatory compliance are critically discussed.

Author Biographies

Diwakar Reddy Peddinti, Independent Software Engineer, USA

Diwakar Reddy Peddinti is a Senior Engineer and Technical Lead with approximately 14 years of experience designing, building, and delivering software systems across a range of industries, including healthcare, aviation and airlines, industrial systems, and supply chain tracking. In his current role at Trackonomy Systems, he leads engineering on large-scale asset-tracking and IoT platforms used in logistics and supply chain operations. Across his career he has taken projects from initial concept through production deployment, pairing hands-on engineering with technical leadership by mentoring engineers, setting architectural direction, and coordinating cross-functional teams to ship reliable, real-world systems.

Saurabh Prakash Shetty, Independent Senior Mobile Software Engineer, USA

Saurabh Shetty is a Senior Mobile Software Engineer with over 10 years of professional experience in the design, architecture, and development of mobile applications. His career spans venture-backed startups, Fortune 1 organizations including Walmart, and federal technology initiatives supporting agencies such as the U.S. Food and Drug Administration (FDA) and the U.S. Department of Agriculture (USDA). Throughout his career, he has developed solutions across Android, iOS, and cross-platform mobile ecosystems, with experience in enterprise mobility, IoT, AI-enabled applications, and cloud-connected systems.

His research interests include mobile computing, software architecture, artificial intelligence, Internet of Things (IoT), enterprise mobile systems, and user-centered application design. He holds a Master of Science in Computer Science from The George Washington University.

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