An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Abstract

This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry Transport) attacks using the Intrusion Detection and Prevention System (IDPS) for edge nodes. Because the Machine Learning (ML) model cannot be trained directly on lowperformance platforms (such as edge devices),a new methodology for updating ML models is proposed to provide a tradeoff between the model performance and the computational complexity. The proposed methodology involves training the model on a high-performance computing platform and then installing the trained model as a detection engine on low-performance platforms (such as the edge node of the edge layer)to identify new attacks. Multiple security techniques have been employed in the second half of the manuscript to verify that the exchanged trained model and the exchanged data files are valid and undiscoverable (information authenticity and privacy) and that the source (such as a fog node or edge device) is indeed what it it claimed to be (source authentication and message integrity). Finally, the proposed security paradigm is found to be effective against various internal and external threats and can be applied to a low-cost singleboard computer (SBC).

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

“…Brute-Force Attack: The impact of this type of attack on IoT networks has become increasingly prevalent and results in significant harmful consequences [ 31 ]. During the authentication process, a brute-force attack aims to decipher the authentication credentials of users’ information, such as usernames and passwords [ 32 ].…”

“…Malformed Attack: The MQTT protocol is vulnerable to exploitation and targeting of specific categories of single-packet attacks known as malformed attacks. This type of attack aims to produce and send many inaccurate, malformed packets to the broker to trigger exceptions within the determined service [ 32 ].…”

“…Flood Attack: This type of attack can be employed against the MQTT protocol and different network services. Its purpose is to obstruct the creation of services from law-abiding customers [ 32 ].…”

Integrating self-powered disaster recovery networks with environmental monitoring for enhanced disaster preparedness and response

“…These findings underscore the effectiveness of the proposed power management scheme in extending the lifespan of solar energy-harvested battery-based roadside units, enhancing their reliability and availability, which positively impacts the construction of a dependable DRN infrastructure. Next, we will assess the performance of the proposed power management technique in comparison to traditional duty cycling methods, namely Fixed Duty Cycling (FDC) [18] and Artificial Neural Networkbased Adaptive Duty Cycling (ANN-ADC) [17] . The evaluation of these methods is based on the following metrics:…”

Enhancing and Securing Telemedicine Services in Disaster Recovery Operations with Sustainable Communication Infrastructure and Blockchain Technology

“…Meanwhile, contemporary m-health telemedicine research delves into diverse topics, including Medical Video Modality-Aware (m-aware) systems, which adapt encoding, transmission, and evaluation based on individual video modality properties [5][6][7][8][9]. Multilayer and cross-layer optimization systems aim for optimal performance [10][11][12][13], alongside studies focusing on clinical quality assessment protocols and recommendations [14][15][16][17][18][19][20][21][22][23].…”