Implementing Real-Time Anomaly Detection in Mobile Security Logs Using Artificial Intelligence
Year: 2025
Type: article
Abstract: With the increasing reliance on mobile applications, security threats such as unauthorized access, malware attacks, and network intrusions have become more sophisticated. Traditional rule-based security mechanisms often fail to detect novel and evolving threats, necessitating the integration of Artificial Intelligence (AI) for real-time anomaly detection. This study proposes an AI-driven framework for identifying anomalies in mobile security logs using machine learning (ML) and deep learning (DL) models. The research employs Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks to analyze security log patterns and detect malicious behavior dynamically. The results demonstrate that the LSTM-based model outperforms other approaches, achieving 96.8% accuracy with a low false positive rate (3.1%), making it the most effective model for real-time threat detection. The system is integrated with a Flask-based API, enabling mobile applications to transmit logs for continuous monitoring and automated anomaly detection. Despite its effectiveness, the LSTM model requires optimization for real-time processing, and future work will focus on reducing computational overhead through model compression techniques. This study contributes to the advancement of AI-based mobile cybersecurity, providing an adaptive and scalable solution for protecting mobile users against emerging threats.
Author: Mr. Ashish Vishwakarma
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409166302