Deep LearningBased Intrusion Detection for IoT Networks Internet of Things (IoT) has an immense potential for a plethora of applications ranging from healthcare automation to defence networks and the power grid. The security of an IoT network is essentially paramount to the security of the underlying computing and communication infrastructure. However, due to constrained resources and limited computational capabilities, IoT networks are prone to various attacks. Thus, safeguarding the IoT network from adversarial attacks is of vital importance and can be realised through planning and deployment of effective security controls; one such control being an intrusion detection system. In this paper, we present a novel intrusion detection scheme for IoT networks that classifies traffic flow through the application of deep learning concepts. We adopt a newly published IoT dataset and generate generic features from the field information in packet level. We develop a feedforward neural networks model for binary and multiclass classification including denial of service, distributed denial of service, reconnaissance and information theft attacks against IoT devices. Results obtained through the evaluation of the proposed scheme via the processed dataset illustrate a high classification accuracy.

Internet of Things Security Analytics and Solutions with Deep Learning This study presents a new solution applied to defending networks of Internet of Things (IoT) devices. It aims at providing a comprehensive solution to defending the IoT and establishing a protocol for IoT security. Recent attacks that compromised over 120 million devices highlighted the need for enhancing IoT security. This paper introduces the adoption of deep learning for critical security applications by utilizing snapshots of network traffic from nine realworld IoT devices. Furthermore, a set of tools, and in particular, Support Vector Machines (SVM), Random Forest and Deep Neural Network (DNN) algorithms are tested and compared against one another to determine which is the most deployable and provide the highest accuracy of anomaly detection. The obtained results exhibited that all three tested algorithms provided high accuracy. However, the deep neural network provides the highest coefficient of determination compared to the other tested models, making DNN more suitable for this type of applications. Finally, the DNNu0027s learning autonomy feature allows omission of humans from the loop resulting in time efficient realworld algorithm.