Development of A Machine Learning-Based Security Module for Detecting Exploit-Type Attacks in IoT Networks
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Keywords:
Machine Learning; IoT Networks; Security Module; Exploit-Type AttacksAbstract
This study investigated the effectiveness of a two-layer neural network (NN) model in detecting malicious-based cyberattacks. The model showed strong classification performance with 0.92 precision, 0.965 recall, and F1 score of 0.92. The SMOTE (Synthetic Minority Over-sampling Technique) method was used to solve the class imbalance problem, which greatly improved the prediction ability of the model. In addition, OneHotEncoding was used to convert categorical variables into binary format, which further improved the accuracy of the model. The results demonstrate the potential of deep learning methods, especially NNs, for cybersecurity tasks, demonstrating their ability to accurately detect complex and deep patterns of malicious usage.
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