چکیده مقاله
This article presents a novel approach to enhancing security and governance in smart cities by leveraging a hybrid Convolutional Neural Network Long Short Term Memory CNN LSTM model tailored for Internet of Things IoT systems With the rapid expansion of IoT devices in urban environments, smart cities face unique challenges in managing real time data and detecting anomalies to prevent security threats Traditional methods struggle to capture both spatial and temporal patterns essential for effective threat detection and predictive maintenance in such dynamic settings Our proposed CNN LSTM method combines CNN's spatial feature extraction capabilities with LSTM's ability to process sequential data, achieving an impressive accuracy of ۹۳% in anomaly detection This model not only enhances proactive threat response but also enables efficient resource allocation, making it a robust solution for sustainable and secure IoT governance in smart green cities
کلیدواژهها
نویسندگان
Ali Zolfaghari Bengar
Islamic Azad University of Central Tehran Branch IAUCTB
Mohammad Hosein Poornoori
Islamic Azad University of Central Tehran Branch IAUCTB
Mohammad Sohrabi
Islamic Azad University of Central Tehran Branch IAUCTB
شیوه ارجاع
Zolfaghari Bengar، Ali و Poornoori، Mohammad Hosein و Sohrabi، Mohammad،1403،A Deep Learning Approach Using Convolutional Neural Networks for Enhanced IoT Security and Governance،بیست و پنجمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات،بابل
ارائهشده در
مجموعه مقالات بیست و پنجمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات27 دی 1403 · بابل