Abstract

In the evolving landscape of the Internet of Things (IoT), effective intrusion detection is paramount for maintaining security and data integrity. This study introduces a hybrid heuristic technique utilizing artificial intelligence for enhancing intrusion detection systems (IDS) in IoT environments. By integrating various machine learning models, the research focuses on training, tuning, and validating a sequential neural network to predict intrusion occurrences based on extensive data analysis. The methodology involves modelling, which starts with training machine learning algorithms to predict labels from features, tuning the models to meet organizational requirements, and validating them using holdout data. Key machine learning techniques explored include logistic regression, k-nearest neighbors (KNN), naive Bayes, support vector machines (SVM), decision trees, random forests, and neural networks. Each technique's applicability to classification tasks, particularly binary and multivariate scenarios, is discussed in the context of enhancing IDS capabilities. A sequential neural network model, comprising multiple dense and dropout layers, was developed and trained with 148,033 parameters to achieve high accuracy and robustness. The architecture's effectiveness in learning intricate patterns associated with malicious activities while avoiding overfitting is emphasized. The study demonstrates the model's proficiency in binary classification tasks, which is critical for distinguishing between normal and anomalous behaviors in IoT systems. The results indicate that the neural network, optimized using the hybrid heuristic approach, shows a significant reduction in validation loss and a steady improvement in accuracy over multiple epochs. Despite initial overfitting signs, the model maintains high performance on unseen data, underscoring the importance of ongoing model assessment and tuning.