Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Zolanvari, Maede; Teixeira, Márcio Andrey; Gupta, Lav; Khan, Khaled M.; Jain, Raj

doi:10.1109/jiot.2019.2912022

Cited by 363 publications

(174 citation statements)

References 24 publications

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“…Conventional security methods are not sufficient as industrial networks are foreseen to be frequently upgraded and their topologies are subject to changes. In this direction, adopting AI-based approaches is indispensable, in order to significantly enhance the network intelligence and security of industrial processes [ 54 ]. Towards this end, the AI algorithms can identify small anomalies in data flows from various sensors, which are the source of industrial data, thus constituting an important enhancement of the cybersecurity within factory halls.…”

Section: Realization Aspects Of Ai-based System Elementsmentioning

confidence: 99%

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas

Simoens

Gkonis

et al. 2020

Sensors

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The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

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Section: Realization Aspects Of Ai-based System Elementsmentioning

confidence: 99%

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas

Simoens

Gkonis

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Zolanvari, Teixeira, Gupta, Khan, and Jain (2019) uses machine learning based anomaly detection in industrial internet of things and thus provides security to the infrastructure. Zolanvari et al (2019) demonstrates their model by deploying backdoor, command injection and structured query language injection attacks and proved their machine learning model performed well against anomalies.…”

Section: Literature Surveymentioning

confidence: 99%

A machine learning approach for imputation and anomaly detection in IoT environment

Radhakrishna

Kumar

Puligadda³

et al. 2020

Expert Systems

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The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

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“…These works obtain the data from different data sources such as logs or system sensors, which allows them to perform analysis for intrusion detection [22] or vulnerability analysis [23]. There are, nonetheless, some proposals for using machine learning to support an incident response process; for example, in [24] the authors propose a system that uses a supervised learning model to analyze different types of attacks, or in [25], where the authors use machine learning models to categorize whether a reported event is an incident or not. None of these pieces of work follows a methodological approach, however, nor do they focus on ensuring the operation of the big data environment itself.…”

Section: Related Workmentioning

confidence: 99%

“…Big data with blockchain [16,17] Big data operation monitor [18,19] Machine learning for security [20][21][22][23] Machine learning for incident response [24,25]…”

Section: Machine Learningmentioning

confidence: 99%

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

et al. 2020

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Big data ecosystems are increasingly important for the daily activities of any type of company. They are decisive elements in the organization, so any malfunction of this environment can have a great impact on the normal functioning of the company; security is therefore a crucial aspect of this type of ecosystem. When approaching security in big data as an issue, it must be considered not only during the creation and implementation of the big data ecosystem, but also throughout its entire lifecycle, including operation, and especially when managing and responding to incidents that occur. To this end, this paper proposes an incident response process supported by a private blockchain network that allows the recording of the different events and incidents that occur in the big data ecosystem. The use of blockchain enables the security of the stored data to be improved, increasing its immutability and traceability. In addition, the stored records can help manage incidents and anticipate them, thereby minimizing the costs of investigating their causes; that facilitates forensic readiness. This proposal integrates with previous research work, seeking to improve the security of big data by creating a process of secure analysis, design, and implementation, supported by a security reference architecture that serves as a guide in defining the different elements of this type of ecosystem. Moreover, this paper presents a case study in which the proposal is being implemented by using big data and blockchain technologies, such as Apache Spark or Hyperledger Fabric.

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Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

Cited by 363 publications

References 24 publications

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

A machine learning approach for imputation and anomaly detection in IoT environment

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

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