IoT network security based on machine learning techniques for DDoS threats mitigations

Robert Abbas, I.A. Ibrahim, Mojtaba Masoudi, Jake Pacione, M. J. Hossain

Research output: Working paper

Abstract

Due to the exponential growth of Internet of Things (IoT) devices in recent years, combined with the often under-securing of such devices, the rise of botnets targeting these devices for recruitment has significantly increased. Once compromised, a part of these botnets can be used to orchestrate devastating distributed denial of service (DDoS) attacks. Unfortunately, imposing standards on such devices is a challenging task because the manufacturing process is cheap and rapid mass. Therefore, another solution to the increasing threat of DDoS attacks must be formed. This paper details a responsive machine learning based solution to detect outgoing malicious traffic such as various DDoS attacks, as well as providing a proof of concept and an interesting sneak peak into the emerging world of software defined security solutions.
Keywords: IoT security, DDoS mitigation, machine learning
Original languageEnglish
Publication statusIn preparation - 8 Mar 2019

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