AI-Driven Anomaly Detection for IoT Devices in Smart Homes using Android-Based Mobile Applications
Keywords:
IoT Security, Smart Homes, Anomaly Detection, Artificial Intelligence, Mobile Application, Android.Abstract
The increasing adoption of Internet of Things (IoT) devices in smart homes has transformed daily living through enhanced automation, remote access, and energy optimization. However, these devices introduce vulnerabilities due to limited computational resources, heterogeneous communication protocols, and weak authentication mechanisms. Traditional security approaches, including signature-based intrusion detection systems, are insufficient to address modern cyber threats such as zero-day attacks, botnet infiltration, and data exfiltration. This paper proposes an AI-driven anomaly detection framework that leverages a hybrid Long Short-Term Memory (LSTM) and Random Forest (RF) model for real-time anomaly detection in IoT ecosystems. The framework is integrated into an Android-based mobile application that provides end-users with anomaly alerts, visualization dashboards, and device isolation controls. The system is evaluated using the TON_IoT dataset, achieving an accuracy of 96.8%, precision of 95.2%, and recall of 94.6%, outperforming traditional machine learning baselines. A usability study with 30 participants confirmed high user satisfaction, scoring an average of 4.6/5 in usability. This work demonstrates the feasibility of combining advanced AI models with mobile interfaces to enhance smart home IoT security, while addressing issues of latency, accessibility, and user experience.
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