Deep learning and big data technologies for IoT security Abstract   Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on stateoftheart deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects.

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.