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The Real-Time IoT Data Security
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AI
DOI：10.1007/978-3-031-97657-5_22.png)
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Abstract
En 中文
Our main area of study is IoT security, including its effects and ways to mitigate them in real time. This study focuses on Distributed Denial-of-Service (DDoS) assaults, which pose a serious risk to Internet of Things networks because of the growing number of unprotected devices. We examine the characteristics of DDoS attacks, examine the strategies to detect it, especially in the African setting, and suggest a detection methodology based on algorithms from Adaptive Resonance Theory (ART) and Long Short-Term Memory (LSTM). The detection performance is evaluated using metrics such as F1-score, precision, and recall. By offering an efficient DDoS detection framework, the study helps to improve real-time IoT data security.
Keywords:
Distributed Denial-of-Service (DDoS)
Real-time Detection
Long Short-Term Memory (LSTM)
Adaptive Resonance Theory (ART)
Machine Learning
Cybersecurity
Network Security
Africa
Journal
C
IF:
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Papers:
27
Citations:
0