Design of a Distributed HIDS for IoT Backbone Components

Kfouri, Guilherme de O.; Gonçalves, Daniel Ronaldo; Dutra, Bruno V.; Alencastro, João F. de; Filho, Francisco L. de Caldas; Martins, Lucas M. C. e; Praciano, Bruno J. G.; Albuquerque, Robson de Oliveira; Sousa, Rafael T. de

doi:10.15439/2019f329

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…The authors experimentally demonstrated the existence of the mimicry attacks and demonstrated the need to prevent them. In [ 96 ], the authors propose a host-based intrusion detection system that was created and prototyped to safeguard IoT devices, that make up IoT network backbones. In their approach, a set of suggested NIDS performs traditional security verification, and it engages with a controller from the host-based NIDS to enable the coordination of intrusion detection activities in response to IoT devices affected by DDoS attacks throughout the network.…”

Section: Background Of Nids In Iot and Their Classificationmentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Gyamfi

Jurcut

2022

Sensors

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The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

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Section: Background Of Nids In Iot and Their Classificationmentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Gyamfi

Jurcut

2022

Sensors

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“…The authors used experimental evidence to show that mimicking attacks do exist and that their prevention is necessary. The authors of [57] suggest a HIDS that was developed and designed to protect IoT devices, which form the core of IoT networks. Their method involves a group of suggested IDS that carry out conventional security verification and interact with HIDS controller to enable the coordination of intrusion detection actions in response to IoT devices targeted by DDoS attacks across the network.…”

Section: Host-based Ids For Iot Systems (Hids)mentioning

confidence: 99%

IoT Based Intrusion Detection Systems from The Perspective of Machine and Deep Learning: A Survey and Comparative Study

Ashraf¹,

Areed²,

Salem³

et al. 2022

Delta University Scientific Journal

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The term "Internet of Things" (IoT) refers to a group of gadgets that are capable of connecting to the Internet in order to gather and share data. The growth of Internet connections and the arrival of new technologies like the Internet of Things (IoT) have increased the privacy and security threats associated with the introduction of various gadgets. In order to increase the detection of cyber-attacks, industries are increasing their research spending. Institutions choose wise testing and verification techniques by comparing the highest rates of accuracy. IoT use has been accelerating recently across a variety of industries, including health care, smart homes, intelligent transportation, smart cities, and smart grids. where technology researchers and developers started to take notice of the IoT possibilities. Unfortunately, the privacy and security concerns imposed on by energy restrictions and the scalability of IoT devices present the most significant challenge to IoT. Therefore, how to address the IoT's security and privacy challenges remains an essential issue in the field of information security. With a decentralized design, edge computing plays a vital role in enabling IoT devices to compute, make decisions, take actions, and push only pertinent information to the cloud. Since sensitive data is more readily available and can be used right away, the IDS performs better when employing machine learning (ML) and deep learning (DL) algorithms to identify and prevent various threats. In terms of technical limitations, this study classifies the current, recent research in IoT intrusion detection systems employing machine learning, deep learning, and edge computing architecture.

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“…All of the processes get compromised by dodging the intrusion detection system with DDOS (distributed Denial of Service). 61 This attack provides access to the network control system of the Tetra Packaging Machine because it has the potential to compromise system authentication mechanisms. The results revealed three critical functionalities of the system having the potential to materialize accidents (as shown in Table 4).…”

Section: Risk Assessment Using Fmea For Tetra Packaging Case Studymentioning

confidence: 99%

“…Once recognized the hazards, the analysis reveals that the security threats (i.e., tempering the calculations of packages temperature, disturbing the evaporation process, stopping the process of gas removal, increasing/decreasing the bottle movement speed, tempering the bottle cleaning process, disturbing the sealing, cutting and wrap molding process) can introduce safety hazards. All of the processes get compromised by dodging the intrusion detection system with DDOS (distributed Denial of Service) 61 . This attack provides access to the network control system of the Tetra Packaging Machine because it has the potential to compromise system authentication mechanisms.…”

Section: Risk Management Process Demonstrated Through the Tetra Packa...mentioning

confidence: 99%

Safety and security risks management process for cyber‐physical systems: A case study

Inayat

Farooq

Inayat

2022

J Software Evolu Process

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Cyber‐physical systems (CPS) are applicable in emerging industries such as health, energy, autonomous vehicles, and Industrial Internet of Things (IIOT). In CPS the physical assets, that is, actuators and sensors, etc. communicate with each other over a messaging protocol. This communication process of CPS makes them vulnerable to cyber‐attacks which challenge the system safety, making security and safety two major concerns for CPS. Both safety and security risks are considered separately in literature. However, the International Society of Automation (ISA) focuses on the alignment of security and safety risks of CPS. Weak or no alignment in safety and security of a system may result in an inefficient or partially protected system which could end up in disasters. This pressed the need for an integrated safety‐security risk management process. For this purpose, we used a tetra packaging case study to (i) examine the vulnerabilities of CPS by running the risk management process, (ii) identify safety‐security requirements, and (iii) align retrieved safety‐security requirements with the relevant standards. The results show (i) safety hazards and security risks along with their severity and priority, (ii) mitigation guidelines are provided by consulting IEC 61508, and (iii) 15 safety‐security requirements are identified and are aligned with ISO9001 Packaging and labeling machine standard.

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Design of a Distributed HIDS for IoT Backbone Components

Cited by 6 publications

References 10 publications

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

IoT Based Intrusion Detection Systems from The Perspective of Machine and Deep Learning: A Survey and Comparative Study

Safety and security risks management process for cyber‐physical systems: A case study

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