The Westermo network traffic data set

Strandberg, Per Erik; Söderman, David; Dehlaghi-Ghadim, Alireza; Leon, Miguel; Markovic, Tijana; Punnekkat, Sasikumar; Moghadam, Mahshid Helali; Buffoni, David

doi:10.1016/j.dib.2023.109512

Cited by 5 publications

(1 citation statement)

References 7 publications

(9 reference statements)

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“…The feature with the highest absolute coefficient for each principal component is the most relevant for that axis. Second, we showcase the architecture feature to handle training on different blocks of the SFI2 architecture using a Westermo dataset in our evaluation [20]. This dataset refers to 90 min of packet industrial network slicing, including harmless SSH, bad SSH, misconfigured IP addresses, duplicated IP addresses, port scans, and man-in-themiddle attacks.…”

Section: Ml-native Security -An Architectural Feature Orchestration Casementioning

confidence: 99%

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Martins,

Moreira,

Silva

et al. 2024

Preprint

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Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature’s orchestration process and benefits, highlighting its importance for the network slicing process.

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Section: Ml-native Security -An Architectural Feature Orchestration Casementioning

confidence: 99%

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Martins,

Moreira,

Silva

et al. 2024

Preprint

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InSecTT Technologies for the Enhancement of Industrial Security and Safety

Punnekkat,

Markovic,

León

et al. 2024

Studies in Computational Intelligence

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The recent advances in digitalization, improved connectivity and cloud based services are making a huge revolution in manufacturing domain. In spite of the huge potential benefits in productivity, these trends also bring in some concerns related to safety and security to the traditionally closed industrial operation scenarios. This paper presents a high-level view of some of the research results and technological contributions of the InSecTT Project for meeting safety/security goals. These technology contributions are expected to support both the design and operational phases in the production life cycle. Specifically, our contributions spans (a) enforcing stricter but flexible access control, (b) evaluation of machine learning techniques for intrusion detection, (c) generation of realistic process control and network oriented datasets with injected anomalies and (d) performing safety and security analysis on automated guided vehicle platoons.

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Intrusion Detection Study of Nuclear Power Plant Electronic Systems Based on Machine Learning Optimization Algorithms

Zhang

2023

2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC)

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The Westermo network traffic data set

Cited by 5 publications

References 7 publications

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

InSecTT Technologies for the Enhancement of Industrial Security and Safety

Intrusion Detection Study of Nuclear Power Plant Electronic Systems Based on Machine Learning Optimization Algorithms

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