Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking

Abstract

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.

IEEE Internet of Things Journal, 2018

References in corpus (5)

• Caffe: Convolutional Architecture for Fast Feature Embedding
• Defeating jamming with the power of silence: a game-theoretic analysis
• Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
• Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
• Strong authentication on smart wireless devices

Cited by in corpus (13)

• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey
• Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions
• Blockchain for the Internet of Things: Present and Future
• A Survey on Security Issues of 5G NR: Perspective of Artificial Dust and Artificial Rain
• Security Issues of Low Power Wide Area Networks in the Context of LoRa Networks
• DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms
• Hacking the Waveform: Generalized Wireless Adversarial Deep Learning
• Intra-Channel Nonlinearity Compensation Based on Second-Order Perturbation Theory
• My SIM is Leaking My Data: Exposing Self-Login Privacy Breaches in Smartphones
• Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks
• Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning
• PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning
• Second-Order Perturbation Theory-Based Digital Predistortion for Fiber Nonlinearity Compensation