首页|New Machine Learning Study Results Reported from Dar Al-Hekma University (A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats)
New Machine Learning Study Results Reported from Dar Al-Hekma University (A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats)
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A new study on artificial intelligence is now available. According to news reporting from Jeddah, Saudi Arabia, by NewsRx journalists, research stated, “Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring.” The news correspondents obtained a quote from the research from Dar Al-Hekma University: “In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications.”
Dar Al-Hekma UniversityJeddahSaudi ArabiaAsiaCybersecurityCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
