A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead

Pedreira, Vítor; Barros, Daniel; Pinto, Pedro

doi:10.3390/s21155189

Cited by 28 publications

(10 citation statements)

References 112 publications

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“…The Internet's openness, boundarylessness, and virtual nature challenge the closed and exclusive concept of sovereignty. In this context, the concept of network sovereignty is created and has an essential impact on the theory of data sovereignty [ 17 ]. Therefore, the theory of data sovereignty mainly includes two connotations of network sovereignty and technological sovereignty.…”

Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross‐Border Data Flow Governance

2022

Journal of Environmental and Public Health

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The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learning algorithm models under the perspective of cross-border data flow governance. In this study, four machine learning algorithms, namely, logistic regression, decision tree, random forest, and GBDT, are used to build an outbound data assessment and evaluation model. The confusion matrix is used to classify the outbound data legitimacy dichotomously. The recall, precision, and F1 scores are evaluated to compare the empirical results of each model. Based on this, a logistic regression-based outbound data risk scoring model is introduced to quantify the outbound data risk at a deeper level and to classify the outbound data risk level for the reference of regulators to make more scientific and reasonable decisions. The experimental results show that the machine learning models can meet the needs and applications of practical work and make accurate predictions of outbound data risks.

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Section: Literature Reviewmentioning

confidence: 99%

[Retracted] Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross‐Border Data Flow Governance

2022

Journal of Environmental and Public Health

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“…Equation (1). The responses of every unit in the intermediate layer is sent to the final layer as output, which consists of 2 units, and the response for the appropriate output class is triggered by the activation function specified for the output layer.…”

Section: Neural Network Classifier Experimentsmentioning

confidence: 99%

“…According to an analysis of attacks the majority of security breaches occur at the network layer. 1 In most cases, malware, denial of service (DoS), and device hacking impair the security aspects of smart devices. As a result, protecting the smart system connected via networks is critical.…”

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confidence: 99%

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Ramaiah

Vanmathi

Ravi

2023

Concurrency and Computation

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SummaryIndustry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the future networking of an increasing number of devices. As crucial as intelligent automation is, it is essential that it be protected. The proliferation of Internet‐enabled gadgets could raise vulnerability to a variety of threats, malware among them. Intruders see a synthesis of factors as a chance to carry out their malicious plan. Keeping sensitive data and information protected from malicious software is a high responsibility for all industries. It is critical to have both a trustworthy approach and a large dataset to work with when constructing a malware traffic classifier. Malware's capacity to elude detection by antivirus programs improves with the day. Because this malware has the potential to compromise the entire network, establishing a malware traffic classifier requires a strong approach. As the number of data increases, the classifier has a harder time distinguishing between benign and malicious network entries. As a result, weighing too many factors is a time‐consuming process. To assist with these types of real‐world challenges, we construct an effective hybrid selection component, which is subsequently followed by a neural network classifier in this research. The Malware traffic classifier provided here selects the principal feature using filter and wrapper techniques. The feature columns provided by the feature selection program are used to construct a neural network‐based binary malware classifier. The given malware traffic classification framework was tested using the MTA‐KDD'19 dataset. We set up an experiment in this investigation to examine the way different feature counts perform using a neural‐based classifier. The suggested framework achieves 96.8% accuracy while just considering the bare minimum of five features, which is a substantial increase over alternative methods.

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“…(vii) Cross-site scripting (XSS): injecting JavaScript code in a web application to modify the display of a web application and force the victim to execute it in his browser; there are many types of it, such as Reflected XSS and DOM XSS [16]. (viii) Insecure deserialization: manipulating the inputs of a web application by deserializing it, modifying it, and serializing it again to compromise the web application [17]. (ix) Using components with known vulnerabilities: stop updating the used component in a web application allows attackers to exploit its known vulnerabilities; this type of vulnerability is found in abundance, especially in CMS web applications [18].…”

Section: Related Workmentioning

confidence: 99%

Web Application Firewall Using Machine Learning and Features Engineering

Shaheed

Kurdy

2022

Security and Communication Networks

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Web application security has become a major requirement for any business, especially with the wide web attacks spreading despite the defensive measures and the continuous development of software frameworks and servers. In this study, we present a proposed model for a web application firewall that used machine learning and features engineering to detect common web attacks. Our proposed model analyses incoming requests to the webserver, parses these requests to extract four features that describe completely HTTP request parts (URL, payload, and headers), and classifies whether a request is normal or an anomaly. We took into consideration the limitation of previous works that use URL and payload only in classification and provided five features that describe and summarize all parts of the HTTP request using features engineering and previous experience in the field of the software security domain. Extracted features are length of request, percentage of characters allowed, percentage of special characters, and attack weight. These features were calculated for four different datasets CSIC 2010, HTTPParams 2015, Hybrid dataset (CSIC 2010 and HTTPParams), and real logs for the compromised web server. We evaluated our proposed model by using these updated datasets with four classification algorithms (Naive Bayes, logistic regression, decision tree, and support vector machine) with two methods (train test split and cross-validation) to negate the probability of overfitting and ensure that features are effective. Features values for a normal request are usually short request length, large allowed character ratio, small special character ratio, and zero attack weight or close to zero. Features values for anomaly requests are large request length, small allowed character percentage, large special character percentage, and very large numerically attack weight. Our proposed model achieved a classification accuracy of 99.6% with datasets used in research studies in this field and 98.8% with datasets of real web servers.

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A Review of Attacks, Vulnerabilities, and Defenses in Industry 4.0 with New Challenges on Data Sovereignty Ahead

Cited by 28 publications

References 112 publications

[Retracted] Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross‐Border Data Flow Governance

[Retracted] Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross‐Border Data Flow Governance

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Web Application Firewall Using Machine Learning and Features Engineering

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