<![CDATA[The Internet of Things (IoT) presents significant security challenges as the number of connected devices continues to grow. One critical approach in developing efficient attack detection systems is the selection of relevant features to reduce model complexity without compromising accuracy. This study evaluates the effectiveness of Autoencoders as a feature reduction method for IoT network intrusion detection systems. Three machine learning algorithms are employed for comparative analysis: K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM). The dataset is evaluated both before and after feature reduction using an Autoencoder, with performance assessed based on accuracy, precision, recall, F1-score, training time, and the number of features. Experimental results demonstrate that the Autoencoder can reduce the number of features by up to 30% without significantly degrading performance. In fact, the NB and SVM models exhibit improvements in both accuracy and training efficiency. The KNN model shows a minimal performance decline, which remains within acceptable limits. Overall, the Autoencoder proves to be an effective method for feature reduction, maintaining or even enhancing detection efficiency and performance. These findings support the use of Autoencoders as an efficient feature selection technique in IoT-based attack detection systems.]]>
Copyrights © 2024
