LeNet – IoT Security Aware Intrusion Detection Framework Using Deep Learning

The rising complexity of cyberthreats created the demand for compelling cybersecurity solutions that along with expanding Internet of Things (IoT) devices has made them indispensable. In the work our proposal is a Deep Learning-Assisted Intrusion Detection System (IDS), based on the LeNet architecture, that can be used for the identification and prevention of potential intrusions in Internet of Things environments. To increase the detection accuracy, we developed our method based on a properly structured preprocessing step comprising normalisation and data augmentation using image-based and QR-based datasets. We take a look at the architecture of LeNet including the pooling and convolutional layers that help to identify important features of the input dataset. The final parts of the model are fully linked layers and a Softmax classifier that predicts the type of anomaly. Performance metrics measure the efficacy of the IDS including precision, recall, accuracy, and F1 score that reveal our method surpass the classical methods with a high margin. We shall describe in this paper how one can train the LeNet model using IoT-specific attack data so that it could be used to detect intrusion in surrounding network space and inject the walled-off attack, thereby facilitating threat prevention. Since the model is GPU accelerated, it converged close to what was able to protect the IoT network effectively from the evolving cyber-attacks and was quite close to its high accuracy and real time efficient performance in terms of time.