CYBER SEC IOT MOBILE-DEEP SECURE: A UNIFIED CYBERSECURITY FRAMEWORK UTILIZING DEEP LEARNING FOR ENHANCED PROTECTION OF MOBILE AND IOT NETWORKS
Keywords:
Cybersecurity, IoT Security, Mobile Networks, Deep Learning, Machine Learning, CyberSecIoTMobile-DeepSecure, Threat Detection, Network SecurityAbstract
The growing interconnectivity between mobile devices and Internet of Things (IoT) networks has exposed significant cybersecurity vulnerabilities, necessitating a more advanced and unified approach to threat management. CyberSecIoTMobile-DeepSecure is a comprehensive security framework designed to address these challenges by combining deep learning techniques, including convolutional neural networks (CNNs) and long short-term memory (LSTM) models, for enhanced threat detection and response across both mobile and IoT ecosystems. The framework preprocesses network traffic data to remove noise and standardize inputs, improving the accuracy of the deep learning models. By leveraging behavioral analytics and real-time anomaly detection, CyberSecIoTMobile-DeepSecure achieves a 96.8% accuracy in identifying sophisticated threats, such as zero-day exploits and network intrusions. The intelligent response mechanism dynamically adjusts security measures based on the threat's severity, helping to minimize system downtime and enhance overall resilience. Experimental results show that CyberSecIoTMobile-DeepSecure reduces detection latency by 28% and lowers packet loss during attacks by 51% compared to traditional machine learning approaches, ensuring rapid and precise defense. Additionally, the framework demonstrates a 36% decrease in energy consumption, making it suitable for resource-constrained IoT devices. By offering seamless integration, adaptive threat management, and robust protection, CyberSecIoTMobile-DeepSecure provides a scalable solution for securing interconnected mobile and IoT networks in diverse and challenging environments.
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