Implementing Real-Time Anomaly Detection in Mobile Security Logs Using Artificial Intelligence

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.

Introduction

The proliferation of mobile devices has significantly enhanced digital connectivity but also increased exposure to sophisticated cyber threats, such as zero-day attacks and polymorphic malware. Traditional, signature-based security systems are no longer adequate. In response, the integration of Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), has become essential for detecting and mitigating these evolving threats in real-time.

Problem Statement

Mobile security logs are rich in information but current methods struggle to detect unknown threats in real-time and suffer from high false positive rates. This research proposes an AI-based anomaly detection system tailored for mobile environments.

Research Objectives

1. Develop a real-time anomaly detection system using ML/DL.

2. Analyze mobile security logs to identify unusual patterns.

3. Compare the efficiency of various unsupervised learning models.

4. Reduce false positives while maintaining high detection accuracy.

Literature Review

Prior studies demonstrate that AI models like LSTM networks, Autoencoders, and hybrid models can effectively detect complex, time-based anomalies in mobile environments. Notable systems include:

• Milosevic & Huang (2019): Used LSTM for Android malware detection.

• Vella & Colombo (2021): Developed "SpotCheck" using KPCA and VAE.

• Mokhtari et al. (2020): Applied hybrid models to detect anomalies in telecom networks.

• iVerify (2024): Combines ML with heuristic detection to identify spyware.

These works confirm the growing necessity and effectiveness of AI in mobile cybersecurity.

Methodology

The study's methodology includes:

1. Data Collection: Using real-world, public, and synthetic datasets (CICIDS 2017, DARPA 1998, Android security logs).

2. Preprocessing & Feature Extraction: Clean and structure data; use PCA and RFE for dimensionality reduction.

3. Modeling: Train and evaluate Isolation Forest, Autoencoders, and LSTM networks.

4. Evaluation Metrics: Accuracy, Precision, Recall, F1-score, and False Positive Rate (FPR).

5. Deployment: The best-performing model (LSTM) is deployed using a Flask API with real-time processing supported by TensorFlow and Scikit-learn.

Implementation

Dataset Sources:

• CICIDS 2017: For network and system call analysis.

• DARPA 1998: For intrusion and access attempts.

• Android Logs: Focus on mobile-specific threats.

• Synthetic Logs: Simulate rare attack scenarios.

Model Training Results:

• LSTM: Best performance (96.8% accuracy, 3.1% FPR).

• Autoencoder: Good performance (94.1% accuracy, 4.6% FPR).

• Isolation Forest: Fastest but least accurate (92.5% accuracy, 5.2% FPR).

Deployment Setup:

• Flask API for integration with mobile apps.

• Real-time processing using AI inference.

• Notifications and log storage handled via AWS S3 and MySQL.

Results & Discussion

Model Comparison:

Detection by Attack Type (LSTM scores highest):

• Unauthorized Access: 97.5%

• Malware Activity: 96.1%

• Network Anomalies: 95.8%

• Data Exfiltration: 96.3%

Processing Efficiency:

• Isolation Forest: Fastest (1.2 sec/1000 logs), but lower accuracy.

• LSTM: Slowest (5.2 sec/1000 logs), best accuracy and reliability.

False Positives/Negatives:

• LSTM: Lowest (FP 3.1%, FN 2.8%)

• Autoencoder: Moderate (FP 4.6%, FN 5.4%)

• Isolation Forest: Highest (FP 5.2%, FN 7.8%)

Conclusion

The study successfully developed an AI-driven real-time anomaly detection system for mobile security logs, demonstrating that deep learning models, particularly LSTM networks, provide superior accuracy in identifying unauthorized access, malware activity, and network anomalies. The evaluation results showed that LSTM-based models achieved high precision and recall while maintaining a low false positive rate, making them the most effective approach for detecting dynamic security threats. Although Autoencoders also performed well, they were slightly less accurate, while Isolation Forest proved to be computationally efficient but less reliable due to higher false positives. Despite its effectiveness, the LSTM model requires optimization to improve real-time processing speed, which remains a challenge for mobile applications with limited computational resources. Future work should focus on model optimization through techniques like quantization, GPU acceleration, and federated learning to enhance efficiency while maintaining detection accuracy. Additionally, integrating hybrid AI models that combine traditional machine learning and deep learning techniques could further improve performance. Exploring privacy-preserving approaches such as differential privacy and edge-based anomaly detection will also be essential to ensure secure and scalable mobile security solutions.

References

[1] Milosevic, N., & Huang, J. (2019). Deep learning guided Android malware and anomaly detection. arXiv preprint arXiv:1910.10660. Retrieved from https://arxiv.org/abs/1910.10660 [2] Sarkar, A., Sen, T., Kundu, S., & Wazed, A. (2022). LogAnMeta: Log Anomaly Detection Using Meta Learning. arXiv preprint arXiv:2212.10992. Retrieved from https://arxiv.org/abs/2212.10992 [3] Vella, T., & Colombo, C. (2021). SpotCheck: An On-Device Machine Learning Framework for Mobile Anomaly Detection. Journal of Cybersecurity and Digital Forensics, 10(3), 127-140. [4] Mokhtari, M., Kharrazi, M., & Amiri, M. (2020). Hybrid AI-Based Intrusion Detection in Mobile Cellular Networks. Elsevier Journal of Network Security, 45(2), 89-103. [5] Canadian Institute for Cybersecurity. (2017). CICIDS 2017 dataset. Retrieved from https://www.unb.ca/cic/datasets/ids.html [6] Defense Advanced Research Projects Agency (DARPA). (1998). DARPA Intrusion Detection Evaluation Data Set. Retrieved from https://www.ll.mit.edu/r-d/datasets [7] iVerify. (2024). AI-Powered Mobile Threat Hunting for iOS and Android. Security Research & Threat Intelligence Reports. [8] TechMagic. (2023). AI-Powered Anomaly Detection for Cybersecurity: Techniques & Use Cases. Retrieved from https://www.techmagic.co/blog/ai-anomaly-detection [9] Arxiv.org. (2023). AI-Driven Techniques for Real-Time Log Analysis and Cybersecurity Threat Detection. arXiv preprint. Retrieved from https://arxiv.org/abs/1910.10660

Copyright

Copyright © 2025 Mr. Ashish Vishwakarma , Prof. Niresh Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.