Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions

Abstract

In a wireless Industrial Internet of Things (IIoT) network, enforcing security is a challenge due to the large number of devices forming the network and their limited computation capabilities. Furthermore, different security attacks require specifically tailored security protocols to prevent their occurrence. As an alternative to these conventional centralized security protocols, the application of Blockchain (BC) and Deep learning (DL) for securing IIoT networks hold great potential. BC facilitates security by being an immutable record of the changes happening in a network. Coalition Formation theory aids decentralization and promotes energy efficiency. And to enforce a state‐of‐the‐art attack detection technique, Deep learning provides an adaptive and reliable platform. Thus, in this paper, a security framework that facilitates generalized security for the IIoT network using BC and Coalition Formation theory is proposed. Additionally, we promote a sophisticated deep learning‐based classification algorithm to efficiently classify malicious and benign devices in IIoT scenarios. In the proposed model, connection links can only be established if the details of the connection are mined on the BC by the “sender” device. Therefore, we propose a Proof of Reliance algorithm that dynamically increases the computational difficulty to prevent malicious devices from attacking the network. Through simulations, it is experimentally proven that malicious devices can never attack the network when the proposed framework is employed for IIoT security.

Blockchain technology for the industrial Internet of Things: A comprehensive survey on security challenges, architectures, applications, and future research directions

“…IIoT is creating new generation smart systems by conducting the digital transformation in traditional industries. IIoT dramatically changed and improved industrial procedures by utilizing the cutting‐edge technologies and introducing the latest business models 6‐8 . IIoT promises to enhance the performance of exiting industrial procedures by optimizing the manufacturing process, reducing cost, and increase the efficiency of smart enterprises.…”

LEOBAT: Lightweight encryption and OTP based authentication technique for securing IoT networks

“…Among these, authentication is one of the security services to be deployed in the network. Techniques based on the Blockchain (M. Sharma et al, 2020), machine learning (Punithavathi et al, 2019), cloud services (D. K. Sharma et al, 2021), biometric (Sui et al, 2011), session keys or session time, cryptography can be utilized for ensuring security via authentication, identity and privacy management. Cryptography is broadly categorized into symmetric key and asymmetric key.…”

Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

“…The presented method accomplished a feasible method for containing and detecting intruders in an IoT. In [13], a security architecture helps comprehensive protection for the IIoT through blockchain (BC) and Coalition Formation concept has been introduced. In addition, we support a traditional DL-based classification method for effectively categorizing benign and malicious devices in IIoT.…”