# Machine Learning in IoT Security: Current Solutions and Future Challenges

## TL;DR

This paper reviews IoT security challenges, current solutions, and highlights how machine learning and deep learning can address existing gaps, proposing future research directions for intelligent security in resource-constrained IoT networks.

## Contribution

It provides a comprehensive review of IoT security issues, analyzes the limitations of traditional methods, and discusses how ML and DL techniques can enhance security solutions.

## Key findings

- ML and DL can improve detection of security threats in IoT.
- Current solutions have gaps due to IoT resource constraints.
- Future research should focus on tailored ML/DL models for IoT security.

## Abstract

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.