The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks.Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques.An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks.
In the presence of unpredictable network technologies Valores de referencia para las pruebas de Cooper y de 20m de ida y vuelta en población residente en altitud elevada. [Reference values for Cooper and Shuttle Run Tests in population living at high altitude]. and various intrusion methods, traditional machine learning techniques appear inefficient.In many research areas, deep learning methods have shown their ability to identify anomalies accurately.Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations.
In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks.First, a convolutional neural network model is used to create a multiclass classification model.The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D.
The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets.Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model.Our proposed binary and multiclass Reconstructing Nonparametric Productivity Networks classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.