A Systematic Literature Review on the NIS2 Directive

• URL: http://arxiv.org/abs/2412.08084v1
• Date: Wed, 11 Dec 2024 04:04:47 GMT
• Title: A Systematic Literature Review on the NIS2 Directive
• Abstract summary: A directive known as NIS2 was enacted in the European Union (EU) in late 2022.<n>The paper presents a systematic literature review on academic research addressing the directive.
• Score: 0.2538209532048867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A directive known as NIS2 was enacted in the European Union (EU) in late 2022. It deals particularly with European critical infrastructures, enlarging their scope substantially from an older directive that only considered the energy and transport sectors as critical. The directive's focus is on cyber security of critical infrastructures, although together with other new EU laws it expands to other security domains as well. Given the importance of the directive and most of all the importance of critical infrastructures, the paper presents a systematic literature review on academic research addressing the NIS2 directive either explicitly or implicitly. According to the review, existing research has often framed and discussed the directive with the EU's other cyber security laws. In addition, existing research has often operated in numerous contextual areas, including industrial control systems, telecommunications, the energy and water sectors, and infrastructures for information sharing and situational awareness. Despite the large scope of existing research, the review reveals noteworthy research gaps and worthwhile topics to examine in further research.

Related papers

• Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges [60.18620798072947]
  Large Language Models (LLMs) have been extensively integrated into recommender systems.<n>LLMs offer unprecedented opportunities to enhance trustworthiness.<n>But they simultaneously introduce new risks, such as novel forms of bias and hallucination-induced issues.
  arXiv  Detail & Related papers  (2026-05-30T05:14:53Z)
• RiskAtlas: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation [53.47466016688839]
  Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare.<n>We propose an end-to-end framework that performs knowledge-graph-guided harmful prompt generation and applies dual-path obfuscation rewriting.<n>This framework yields high-quality datasets combining strong domain relevance with implicitness.
  arXiv  Detail & Related papers  (2026-01-08T09:05:28Z)
• OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment [63.662126457336534]
  OpenNovelty is an agentic system for transparent, evidence-based novelty analysis.<n>It grounds all assessments in retrieved real papers, ensuring verifiable judgments.<n>OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.
  arXiv  Detail & Related papers  (2026-01-04T15:48:51Z)
• In Numeris Veritas: An Empirical Measurement of Wi-Fi Integration in Industry [0.0]
  A critical knowledge gap exists regarding the prevalence and security configuration of Wi-Fi in real-world settings.<n>We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks.<n>Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies.
  arXiv  Detail & Related papers  (2025-09-21T09:01:58Z)
• A NIS2 pan-European registry for identifying and classifying essential and important entities [0.0]
  The NIS2 Directive establishes a common cybersecurity governance model across the European Union.<n>This thesis presents an analysis of the NIS2 Directive in this context and translates its provisions into concrete technical requirements.<n>Using the Design Science Research methodology, the thesis transforms complex legal provisions into structured, deterministic classification algorithms.
  arXiv  Detail & Related papers  (2025-08-26T19:40:33Z)
• From Cyber Security Incident Management to Cyber Security Crisis Management in the European Union [0.19116784879310028]
  Recently, the European Union (EU) has started to consider also the relation between cyber security incidents and cyber security crises. The paper advances the domain of cyber security incident management research by elaborating how European law perceives cyber security crises and their relation to cyber security incidents.
  arXiv  Detail & Related papers  (2025-04-19T08:03:29Z)
• Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
  Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure. This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
  arXiv  Detail & Related papers  (2025-02-27T11:22:25Z)
• AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
  This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability. The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
  arXiv  Detail & Related papers  (2025-02-19T05:58:52Z)
• Which Information should the UK and US AISI share with an International Network of AISIs? Opportunities, Risks, and a Tentative Proposal [0.0]
  The UK AI Safety Institute (UK) and its parallel organisation in the United States (US) take up a unique position in the recently established International Network of jurisdictions. This paper argues that it is in the interest of both institutions to share specific categories of information with the International Network of evaluations.
  arXiv  Detail & Related papers  (2025-02-05T16:49:02Z)
• AI Governance in the Context of the EU AI Act: A Bibliometric and Literature Review Approach [0.0]
  This study analyzed the research trends in AI governance within the framework of the EU AI Act.<n>Our findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape.
  arXiv  Detail & Related papers  (2025-01-08T11:01:11Z)
• The EU AI Act is a good start but falls short [3.4632900249241874]
  The EU AI Act was created to ensure ethical and safe AI development and deployment across the EU.<n>This study aims to identify key challenges and strategies for helping enterprises focus on resources effectively.
  arXiv  Detail & Related papers  (2024-11-13T11:32:37Z)
• Robust Information Retrieval [77.87996131013546]
  robustness of an information retrieval system is increasingly attracting attention. This tutorial aims to generate broader attention to robustness issues in IR, facilitate an understanding of the relevant literature, and lower the barrier to entry for interested researchers and practitioners.
  arXiv  Detail & Related papers  (2024-06-13T07:44:21Z)
• Service Level Agreements and Security SLA: A Comprehensive Survey [51.000851088730684]
  This survey paper identifies state of the art covering concepts, approaches, and open problems of SLA management. It contributes by carrying out a comprehensive review and covering the gap between the analyses proposed in existing surveys and the most recent literature on this topic. It proposes a novel classification criterium to organize the analysis based on SLA life cycle phases.
  arXiv  Detail & Related papers  (2024-01-31T12:33:41Z)
• Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision [0.10923877073891446]
  This paper presents a comprehensive survey of recent advancements in AI-driven threat response systems. The most recent survey covering the AI reaction domain was conducted in 2017. A total of seven research challenges have been identified, pointing out potential gaps and suggesting possible areas of development.
  arXiv  Detail & Related papers  (2023-12-11T09:17:01Z)
• Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community [180.28262433004113]
  Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference. LLMs and humans form a new technical paradigm that is more powerful for information seeking. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023.
  arXiv  Detail & Related papers  (2023-07-19T05:23:43Z)
• An Ontological Approach to Compliance Verification of the NIS 2 Directive [0.0]
  This paper introduces an approach that leverages techniques of semantic representation and reasoning, hence an ontological approach, towards the compliance check with the security measures that textual documents prescribe. The formalisation of entities and relations from the directive, and the consequent improved structuring with respect to sheer prose is dramatically helpful for any organisation through the hard task of compliance verification.
  arXiv  Detail & Related papers  (2023-06-30T09:10:54Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)

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.