Forensics and security issues in the Internet of Things

TL;DR

This paper reviews security and forensic challenges in IoT, highlighting vulnerabilities, potential solutions like blockchain and deep learning, and emphasizing the need for standardized security measures to protect users and infrastructure.

Contribution

It provides a comprehensive review of IoT security and forensic issues, proposing future research directions and potential solutions such as blockchain and deep learning techniques.

Findings

Most IoT devices lack standardized security measures.

Blockchain can enhance security across trust domains.

Deep learning enables effective cyberattack detection.

Abstract

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed to investigate and mitigate such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Prospects and challenges in IoT research and development are also highlighted. As the literature demonstrates, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfill the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing the security-conscious needs of consumers; IoT can be used to create a smart home system by designing an IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. A blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.