Drones Face off: Authentication by Machine Learning in Autonomous IoT Systems Autonomous InternetofThings (IoT) are comprised of moving objects such as drones and rovers that use selfcontrol techniques to accomplish a mission while following a path. However, losing control in such systems usually by spoofing their sensors or hijacking with misleading commands can lead to catastrophic safety consequences. In this paper, we close the gap by authenticating the behavior of autonomous IoT systems during operation. In particular, we check the behavior of a moving IoT object, e.g., a drone, by evaluating its timeseries telemetry traces during the flight. We examine three different machinelearning algorithms for this purpose, namely, KNearest Neighbour (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Our results show that KNN is the best method of the three selected techniques for authentication in dynamic IoT systems, e.g., drones. We achieved 93.4% in precision rate and 100% recall rate with KNN.

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.