Automated Microsegmentation for Lateral Movement Prevention in Industrial Internet of Things (IIoT)

Arifeen, Murshedul; Petrovski, Andrei; Petrovski, Sergei

doi:10.1109/sin54109.2021.9699232

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The drawbacks of this approach are high operational complexity, lack of automation, significant human involvement required, complex communication mapping for each specific software on the 7th layer of the OSI model, potential communication inaccessibility in case of poor MSG implementation, and risks to reliability and stability. In response to these challenges, an automated MSG model that uses machine learning algorithms to automatically generate micro-segments and separates normal traffic while limiting redundant links and blocking malicious traffic was explored by Arifeen et al (2021). It is particularly suitable for large and dynamic networks, such as the Industrial Internet of Things (IIoT).…”

Section: Device Sidementioning

confidence: 99%

Dissecting zero trust: research landscape and its implementation in IoT

Liu,

Tan,

et al. 2024

Cybersecurity

View full text Add to dashboard Cite

As a progressive security strategy, the zero trust model has attracted notable attention and importance within the realm of network security, especially in the context of the Internet of Things (IoT). This paper aims to evaluate the current research regarding zero trust and to highlight its practical applications in the IoT sphere through extensive bibliometric analysis. We also delve into the vulnerabilities of IoT and explore the potential role of zero trust security in mitigating these risks via a thorough review of relevant security schemes. Nevertheless, the challenges associated with implementing zero trust security are acknowledged. We provide a summary of these issues and suggest possible pathways for future research aimed at overcoming these challenges. Ultimately, this study aims to serve as a strategic analysis of the zero trust model, intending to empower scholars in the field to pursue deeper and more focused research in the future.

show abstract

Section: Device Sidementioning

confidence: 99%

Dissecting zero trust: research landscape and its implementation in IoT

Liu,

Tan,

et al. 2024

Cybersecurity

View full text Add to dashboard Cite

show abstract

“…It is a classification algorithm to classify data into separate classes when the response variable is categorical [11]. LR aims to find a relationship between properties and the probability of a given outcome [27]. A DT functions by removing representative items from a data collection and arranging them in trees according to the object's value [11,28].…”

Section: Machine Learning For Iot Securitymentioning

confidence: 99%

“…The studies of [14,47] dealt with binary, multiple, and branching classifications, and the studies of [12,24] dealt with binary and multiple classifications, as shown in Table 3. In contrast, the studies of [27,33] dealt with binary classification; Table 3 presents the accuracy-based comparison for the three classification levels.…”

Section: Figure 6: Performance Of Classification Models In Subcategor...mentioning

confidence: 99%

Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques

Doghramachi,

Ameen

2023

CMC

View full text Add to dashboard Cite

The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a methodology that involves dataset preprocessing, including data cleaning, encoding, and normalization. The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique (SMOTE) and performing feature reduction using correlation analysis. Multiple ML classifiers, including Logistic Regression, multi-layer perceptron, Decision Trees, Random Forest, and XGBoost, are employed to model IoT attacks. The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset, which represents current imbalanced IoT scenarios. The results highlight that the XGBoost model, integrated with SMOTE, achieves outstanding attack detection accuracy of 0.99 in binary classification, 0.99 in multi-class classification, and 0.81 in multiple sub-classifications. These findings demonstrate our approach's significant improvements to attack detection in imbalanced IoT datasets, establishing its superiority over existing IDS frameworks.

show abstract

“…Trabalhos anteriores incorporam o aprendizado de máquina no monitoramento de ambientes através de detecc ¸ão de intrusão de rede (IDS) [Di Mauro et al, 2021]. Logo, é possível estabelecer perímetros em ambientes micro-segmentados, evitando ataques laterais em recursos compartilhados [Arifeen et al, 2021]. O principal desafio da selec ¸ão de características para classificadores, recai sob a qualidade de informac ¸ão que cada característica agrega ao modelo.…”

Section: Introduc ¸ãOunclassified

Seleção de Características com Alta Quantidade de Informação para Sistemas de Detecção de Intrusão baseada no Conjunto de Dominância de Pareto

Barbosa¹,

Mattos²

2023

Anais Do XLI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2023)

View full text Add to dashboard Cite

A pandemia de COVID-19 impulsionou a mudança no perfil de uso da Internet, o que fomentou o aumento de ataques e novas ameaças a instituições, até então, pouco visadas. Nesse novo cenário, ferramentas de detecção e prevenção de ameaças tendem a ser substituídas por soluções baseadas em aprendizado de máquina, que exigem execução eficiente. Este artigo propõe um método eficiente para a seleção de características para o aprendizado de máquina, utilizando a fronteira de Pareto. A proposta minimiza a correlação de Pearson e a Informação Mútua entre pares de características selecionadas. As características dominantes selecionadas foram aplicadas a três modelos de aprendizado de máquinas para classificação de fluxos maliciosos. O método proposto apresentou eficiência quando comparado a outros métodos, pois permite utilizar menos características para atingir valores similares de acurácia, precisão e revocação, diminuindo o tempo de treinamento e validação.

show abstract

Automated Microsegmentation for Lateral Movement Prevention in Industrial Internet of Things (IIoT)

Cited by 8 publications

References 17 publications

Dissecting zero trust: research landscape and its implementation in IoT

Dissecting zero trust: research landscape and its implementation in IoT

Internet of Things (IoT) Security Enhancement Using XGboost Machine Learning Techniques

Seleção de Características com Alta Quantidade de Informação para Sistemas de Detecção de Intrusão baseada no Conjunto de Dominância de Pareto

Contact Info

Product

Resources

About