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Given the increased growing of Internet of Things networks and their presencein critical aspects of human activities, the security of devices connected tothese networks becomes critical. Machine Learning approaches are becomingprominent as enablers for security solutions in computer networks due to itscapacity to process traffic information in order to detect abnormal patternswhich might represent attacks targeting infrastructures. In this paper, wepropose to leverage Convolutional and Recurrent Neural Networks, two artifactsthat have been successfully used in contexts such as image processing forpattern recognition, for the development of a security solution to be used inthe context of Internet of Things. Our results show that this approach, whenevaluated with a state-of-the-art data set, achieves around 99% of accuracy inthe binary classification of attacks (i.e. normal traffic vs attack traffic)and 96% for multiclass classification (recognition of different types ofattacks) accuracy. These results outperform proposals available in literature,showing a promising landscape for developing security solutions for IoTinfrastructures.

A Method Based on Deep Learning for the Detection and Characterization of Cybersecurity Incidents in Internet of Things Devices