Market Research on IIoT Standard Compliance Monitoring Providers and deriving Attributes for IIoT Compliance Monitoring
- URL: http://arxiv.org/abs/2311.09991v1
- Date: Thu, 16 Nov 2023 16:08:52 GMT
- Title: Market Research on IIoT Standard Compliance Monitoring Providers and deriving Attributes for IIoT Compliance Monitoring
- Authors: Daniel Oberhofer, Markus Hornsteiner, Stefan Schönig,
- Abstract summary: This paper conducts a market study on providers implementing IEC 62443 in IIoT.
It aims to formulate a catalog of monitorable attributes aligned with the standard.
The study reveals challenges, such as a lack of formal separation in security architectures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Adapting security architectures to common standards like IEC 62443 or ISO 27000 in the Industrial Internet of Things (IIoT) involves complex processes and compliance reports. Automatic monitoring of compliance status would enhance this process. Despite limited research, practical applications exist. This paper conducts a market study on providers implementing IEC 62443 in IIoT, aiming to formulate a catalog of monitorable attributes aligned with the standard. The study reveals challenges, such as a lack of formal separation in security architectures, limiting visibility. Despite these challenges, practical implementations share commonalities, providing insights into viable monitoring properties. The research serves as a crucial entry point into developing a comprehensive catalog of monitorable attributes for IEC 62443 standards in IIoT. Aligned with the IEC 62443 SR catalog of document 3-3, monitorable attributes are derived based on current research about IIoT security and Expert Knowledge. The provided tables serve as an exemplary extract, not exhaustive, defining three types of attributes based on their origin of creation.
Related papers
- Testing Resource Isolation for System-on-Chip Architectures [0.9176056742068811]
Ensuring resource isolation at the hardware level is a crucial step towards more security inside the Internet of Things.
We illustrate the modeling aspects in test generation for resource isolation, namely modeling the behavior and expressing the intended test scenario.
arXiv Detail & Related papers (2024-03-27T16:11:23Z) - Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models [48.9044202022435]
Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues.
One of the current alignment techniques includes principle-driven integration, but it faces challenges arising from the imprecision of manually crafted rules.
We introduce Guide-Align, a two-stage approach to address these challenges.
arXiv Detail & Related papers (2024-03-18T14:48:29Z) - Qualitative Analysis for Validating IEC 62443-4-2 Requirements in
DevSecOps [0.8874671354802572]
This paper focuses on the automated validation of ISA/ IEC 62443-4-2 standard component requirements.
Our analysis demonstrates the coverage established by the currently available tools and sheds light on current gaps to achieve full automation.
arXiv Detail & Related papers (2023-10-13T10:24:58Z) - Layered Security Guidance for Data Asset Management in Additive Manufacturing [0.0]
This paper proposes leveraging the National Institute of Standards and Technology's Cybersecurity Framework to develop layered, risk-based guidance for fulfilling specific security outcomes.
The authors believe implementation of the layered approach would result in value-added, non-redundant security guidance for AM that is consistent with the preexisting guidance.
arXiv Detail & Related papers (2023-09-28T20:48:40Z) - Performance Analysis of Security Certificate Management System in
Vehicle-to-Everything (V2X) [0.0]
This study implements end entities and a Security Credential Management System conforming to IEEE 1609.2 and IEEE 1609.2.1 standards.
It measures the computation and transmission times for each security communication action within the system from the perspective of end entities.
arXiv Detail & Related papers (2023-09-18T02:24:33Z) - Online Safety Property Collection and Refinement for Safe Deep
Reinforcement Learning in Mapless Navigation [79.89605349842569]
We introduce the Collection and Refinement of Online Properties (CROP) framework to design properties at training time.
CROP employs a cost signal to identify unsafe interactions and use them to shape safety properties.
We evaluate our approach in several robotic mapless navigation tasks and demonstrate that the violation metric computed with CROP allows higher returns and lower violations over previous Safe DRL approaches.
arXiv Detail & Related papers (2023-02-13T21:19:36Z) - Relational Action Bases: Formalization, Effective Safety Verification,
and Invariants (Extended Version) [67.99023219822564]
We introduce the general framework of relational action bases (RABs)
RABs generalize existing models by lifting both restrictions.
We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes.
arXiv Detail & Related papers (2022-08-12T17:03:50Z) - Creating Training Sets via Weak Indirect Supervision [66.77795318313372]
Weak Supervision (WS) frameworks synthesize training labels from multiple potentially noisy supervision sources.
We formulate Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels.
We develop a probabilistic modeling approach, PLRM, which uses user-provided label relations to model and leverage indirect supervision sources.
arXiv Detail & Related papers (2021-10-07T14:09:35Z) - SMT-Based Safety Verification of Data-Aware Processes under Ontologies
(Extended Version) [71.12474112166767]
We introduce a variant of one of the most investigated models in this spectrum, namely simple artifact systems (SASs)
This DL, enjoying suitable model-theoretic properties, allows us to define SASs to which backward reachability can still be applied, leading to decidability in PSPACE of the corresponding safety problems.
arXiv Detail & Related papers (2021-08-27T15:04:11Z) - I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage
Object Detectors [64.93963042395976]
Implicit Instance-Invariant Network (I3Net) is tailored for adapting one-stage detectors.
I3Net implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers.
Experiments reveal that I3Net exceeds the state-of-the-art performance on benchmark datasets.
arXiv Detail & Related papers (2021-03-25T11:14:36Z) - Multisource AI Scorecard Table for System Evaluation [3.74397577716445]
The paper describes a Multisource AI Scorecard Table (MAST) that provides the developer and user of an artificial intelligence (AI)/machine learning (ML) system with a standard checklist.
The paper explores how the analytic tradecraft standards outlined in Intelligence Community Directive (ICD) 203 can provide a framework for assessing the performance of an AI system.
arXiv Detail & Related papers (2021-02-08T03:37:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.