Secure Industrial Internet of Things Critical Infrastructure Node Design

McGinthy, Jason M.; Michaels, Alan J.

doi:10.1109/jiot.2019.2903242

Cited by 40 publications

(26 citation statements)

References 62 publications

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“…Topic 8 is related to the development of energy-efficient routing protocols that minimize the transmission power for routing between nodes in IoT networks. For instance, a routing protocol for low-power and lossy networks (RPL), a protocol for low-power and low-loss networks, and corresponding security methods have been developed [242][243][244][245][246][247][248][249][250][251][252][253].…”

Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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“…The wide applicability of the IoT devices allows to technify fields of the industry whose technological maturity has been relatively low. Some of the most relevant examples are associated with oil exploitation and electricity production, both fields directly associated with the national cyberdefence [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the design of technology and architectural solutions associated with new production techniques has had to consider the possibility of strengthening IoT communications and devices to ensure the availability, integrity, and availability of information deployed on these resources. In these cases, it is necessary to establish methodologies and procedures in charge of establishing countermeasures, safeguards, and continuities that allow for the control of data cybersecurity [1,3].…”

Section: Introductionmentioning

confidence: 99%

“…The need to protect and classify the information inside the IoT infrastructure by the owners of the technology has awakened in the attackers the investigation of new techniques and mechanisms to obtain, manipulate, extract or eliminate the information processed there. This situation is completely unacceptable for the critical cybernetic infrastructures of a nation, where the damage to a portion of the technology can generate catastrophic consequences in legal, economic, social, and even environmental terms, for the entire state [1,4].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach

et al. 2021

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Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS.

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“…Although they can reflect an enhanced level of security capability due to the fine theoretical and experimental analysis, their applicability and feasibility in real-world IIoT systems await verification by future explorations. The main causes involve the following two aspects: on the one hand, most IIoT devices only have low power and limited system resources, and the security add-ons may decrease their work performance by performing the higher or lower computational costs of security operations [20]; on the other hand, IIoT is usually designed to serve industrial control systems, whose requirements on high availability and reliability may be not completely satisfied because of the inefficient adaption between the original system design and some added security services. Differently, anomaly detection in IIoT systems can be widely regarded as a feasible and effective measure to identify unexpected industrial activities [8,[21][22][23], because it can scarcely affect industrial availabilities and real-time requirements by using the bypass monitoring.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

Zhao

Zeng

Wan

et al. 2021

Wireless Communications and Mobile Computing

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IIoT (Industrial Internet of Things) has gained considerable attention and has been increasingly applied due to its ubiquitous sensing and communication. However, the sparse characteristic of sensing data in distributed IIoT networks may bring out tremendous challenges to implement the security protection measures. Based on the design of centralized data gathering and forwarding, this paper proposes a novel anomaly detection approach for IIoT sparse data, which can successfully collaborate the adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) feature exploitation with one intelligent optimizing classification. Furthermore, in the adaptive CEEMDAN feature exploitation, the CEEMDAN energy entropy based on adaptive IMF (Intrinsic Mode Function) selection is designed to extract the sensing features from IIoT sparse data; in the intelligent optimizing classification, one effective OCSVM (One-Class Support Vector Machine) classifier optimized by the IABC (Improved Artificial Bee Colony) swarm intelligence algorithm is introduced to detect various abnormal sensing features. The experimental results show that, not only does the CEEMDAN energy entropy based on adaptive IMF selection accurately describe the change of industrial production by analyzing the probability distribution and energy distribution of sparse sensing data, but also the proposed IABC-OCSVM classifier has higher detection efficiency compared with the OCSVM classifiers optimized by other swarm intelligence algorithms.

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Secure Industrial Internet of Things Critical Infrastructure Node Design

Cited by 40 publications

References 62 publications

Current Research Trends in IoT Security: A Systematic Mapping Study

Current Research Trends in IoT Security: A Systematic Mapping Study

Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach

Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data

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