Assessing The Effectiveness Of Current Cybersecurity Regulations And Policies In The US
- URL: http://arxiv.org/abs/2404.11473v1
- Date: Wed, 17 Apr 2024 15:26:55 GMT
- Title: Assessing The Effectiveness Of Current Cybersecurity Regulations And Policies In The US
- Authors: Ejiofor Oluomachi, Akinsola Ahmed, Wahab Ahmed, Edozie Samson,
- Abstract summary: The study evaluates the impact of these regulations on different sectors and analyzes trends in cybercrime data from 2000 to 2022.
The findings highlight the challenges, successes, and the need for continuous adaptation in the face of evolving cyber threats.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This article assesses the effectiveness of current cybersecurity regulations and policies in the United States amidst the escalating frequency and sophistication of cyber threats. The focus is on the comprehensive framework established by the U.S. government, with a spotlight on the National Institute of Standards and Technology (NIST) Cybersecurity Framework and key regulations such as HIPAA, GLBA, FISMA, CISA, CCPA, and the DOD Cybersecurity Maturity Model Certification. The study evaluates the impact of these regulations on different sectors and analyzes trends in cybercrime data from 2000 to 2022. The findings highlight the challenges, successes, and the need for continuous adaptation in the face of evolving cyber threats
Related papers
- Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report [50.268821168513654]
We present Foundation-Sec-8B, a cybersecurity-focused large language model (LLMs) built on the Llama 3.1 architecture.
We evaluate it across both established and new cybersecurity benchmarks, showing that it matches Llama 3.1-70B and GPT-4o-mini in certain cybersecurity-specific tasks.
By releasing our model to the public, we aim to accelerate progress and adoption of AI-driven tools in both public and private cybersecurity contexts.
arXiv Detail & Related papers (2025-04-28T08:41:12Z) - 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) - Frontier AI's Impact on the Cybersecurity Landscape [42.771086928042315]
This paper presents an in-depth analysis of frontier AI's impact on cybersecurity.
We first define and categorize the marginal risks of frontier AI in cybersecurity.
We then systemically analyze the current and future impacts of frontier AI in cybersecurity.
arXiv Detail & Related papers (2025-04-07T18:25:18Z) - AI threats to national security can be countered through an incident regime [55.2480439325792]
We propose a legally mandated post-deployment AI incident regime that aims to counter potential national security threats from AI systems.
Our proposed AI incident regime is split into three phases. The first phase revolves around a novel operationalization of what counts as an 'AI incident'
The second and third phases spell out that AI providers should notify a government agency about incidents, and that the government agency should be involved in amending AI providers' security and safety procedures.
arXiv Detail & Related papers (2025-03-25T17:51:50Z) - Integrating Cybersecurity Frameworks into IT Security: A Comprehensive Analysis of Threat Mitigation Strategies and Adaptive Technologies [0.0]
The cybersecurity threat landscape is constantly actively making it imperative to develop sound frameworks to protect the IT structures.
This paper aims to discuss the application of cybersecurity frameworks into the IT security with focus placed on the role of such frameworks in addressing the changing nature of cybersecurity threats.
The discussion also singles out such technologies as Artificial Intelligence (AI) and Machine Learning (ML) as the core for real-time threat detection and response mechanisms.
arXiv Detail & Related papers (2025-02-02T03:38:48Z) - Countering Autonomous Cyber Threats [40.00865970939829]
Foundation Models present dual-use concerns broadly and within the cyber domain specifically.
Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations.
This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
arXiv Detail & Related papers (2024-10-23T22:46:44Z) - Comparative Survey of Cyber-Threat and Attack Trends and Prediction of Future Cyber-Attack Patterns [0.0]
Cyber security breaches are constantly on the rise with huge uncertainty and risks.
The diversity of attacks and growing state actors involvement without any sort of regulation is making cyber weapons attractive to the states.
States are leveraging the anonymity and attribution flaws to hit hard on perceived adversaries.
arXiv Detail & Related papers (2024-10-04T19:06:42Z) - Navigating the road to automotive cybersecurity compliance [39.79758414095764]
The automotive industry is compelled to adopt robust cybersecurity measures to safeguard both vehicles and data against potential threats.
The future of automotive cybersecurity lies in the continuous development of advanced protective measures and collaborative efforts among all stakeholders.
arXiv Detail & Related papers (2024-06-29T16:07:48Z) - A Comprehensive Analytical Review on Cybercrime in West Africa [0.0]
West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts.
This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework.
Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies.
arXiv Detail & Related papers (2024-01-07T23:36:43Z) - Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models [41.068780235482514]
This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants.
CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks.
arXiv Detail & Related papers (2023-12-07T22:07:54Z) - The New Frontier of Cybersecurity: Emerging Threats and Innovations [0.0]
The research delves into the consequences of these threats on individuals, organizations, and society at large.
The sophistication and diversity of these emerging threats necessitate a multi-layered approach to cybersecurity.
This study emphasizes the importance of implementing effective measures to mitigate these threats.
arXiv Detail & Related papers (2023-11-05T12:08:20Z) - The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
The development and regulation of AI seems to have reached a critical stage.
Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4.
This paper analyses the most advanced legal proposal, the European Union's AI Act.
arXiv Detail & Related papers (2023-11-03T12:51:37Z) - A Systematization of Cybersecurity Regulations, Standards and Guidelines
for the Healthcare Sector [5.121113572240309]
This paper contributes a systematization of the significant cybersecurity documents relevant to the healthcare sector.
We collected the 49 most significant documents and used the NIST cybersecurity framework to categorize key information.
arXiv Detail & Related papers (2023-04-28T16:19:21Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - The Opportunity to Regulate Cybersecurity in the EU (and the World):
Recommendations for the Cybersecurity Resilience Act [1.2691047660244335]
Safety is becoming cybersecurity under most circumstances.
This should be reflected in the Cybersecurity Resilience Act when it is proposed and agreed upon in the European Union.
It is based on what the cybersecurity research community for long have asked for, and on what constitutes clear hard legal rules instead of soft.
arXiv Detail & Related papers (2022-05-26T07:20:44Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
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.