Handling Pandemic-Scale Cyber Threats: Lessons from COVID-19
- URL: http://arxiv.org/abs/2408.08417v1
- Date: Thu, 15 Aug 2024 20:59:23 GMT
- Title: Handling Pandemic-Scale Cyber Threats: Lessons from COVID-19
- Authors: Adam Shostack, Josiah Dykstra,
- Abstract summary: We analyze six critical lessons from COVID-19, outlining key considerations for successful preparedness.
We emphasize the need for developing similar doctrine and skill sets for cyber threats.
- Score: 1.2354076490479515
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The devastating health, societal, and economic impacts of the COVID-19 pandemic illuminate potential dangers of unpreparedness for catastrophic pandemic-scale cyber events. While the nature of these threats differs, the responses to COVID-19 illustrate valuable lessons that can guide preparation and response to cyber events. Drawing on the critical role of collaboration and pre-defined roles in pandemic response, we emphasize the need for developing similar doctrine and skill sets for cyber threats. We provide a framework for action by presenting the characteristics of a pandemic-scale cyber event and differentiating it from smaller-scale incidents the world has previously experienced. The framework is focused on the United States. We analyze six critical lessons from COVID-19, outlining key considerations for successful preparedness, acknowledging the limitations of the pandemic metaphor, and offering actionable steps for developing a robust cyber defense playbook. By learning from COVID-19, government agencies, private sector, cybersecurity professionals, academic researchers, and policy makers can build proactive strategies that safeguard critical infrastructure, minimize economic damage, and ensure societal resilience in the face of future cyber events.
Related papers
- Analysing India's Cyber Warfare Readiness and Developing a Defence Strategy [0.0]
The demand for strong cyber defence measures grows, especially in countries such as India.
The literature review reveals significant shortcomings in India's cyber defence readiness.
The study proposes an educational framework for training cyber professionals.
arXiv Detail & Related papers (2024-06-18T12:55:07Z) - Threat analysis and adversarial model for Smart Grids [1.7482569079741024]
The cyber domain of this smart power grid opens a new plethora of threats.
Different stakeholders including regulation bodies, industry and academy are making efforts to provide security mechanisms to mitigate and reduce cyber-risks.
Recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats.
This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals.
arXiv Detail & Related papers (2024-06-17T16:33:46Z) - SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence [27.550484938124193]
This paper introduces a framework to benchmark, elicit, and improve cybersecurity incident analysis and response abilities.
We create a high-quality bilingual instruction corpus by crawling cybersecurity raw text from cybersecurity websites.
The instruction dataset SEvenLLM-Instruct is used to train cybersecurity LLMs with the multi-task learning objective.
arXiv Detail & Related papers (2024-05-06T13:17:43Z) - A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors [1.715270928578365]
This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors.
We propose a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95%)
We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets.
arXiv Detail & Related papers (2024-03-28T09:41:24Z) - 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) - Physical Adversarial Attack meets Computer Vision: A Decade Survey [57.46379460600939]
This paper presents a comprehensive overview of physical adversarial attacks.
We take the first step to systematically evaluate the performance of physical adversarial attacks.
Our proposed evaluation metric, hiPAA, comprises six perspectives.
arXiv Detail & Related papers (2022-09-30T01:59:53Z) - Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the
Age of AI-NIDS [70.60975663021952]
We study blackbox adversarial attacks on network classifiers.
We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions.
We show that a continual learning approach is required to study attacker-defender dynamics.
arXiv Detail & Related papers (2021-11-23T23:42:16Z) - 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) - Cyber Security in the Age of COVID-19: A Timeline and Analysis of
Cyber-Crime and Cyber-Attacks during the Pandemic [2.9555437538581053]
This paper analyses the COVID-19 pandemic from a cyber-crime perspective.
It highlights the range of cyber-attacks experienced globally during the pandemic.
arXiv Detail & Related papers (2020-06-21T22:53:47Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z) - When Wireless Communication Faces COVID-19: Combating the Pandemic and
Saving the Economy [93.08344893433639]
The year 2020 is experiencing a global health and economic crisis due to the COVID-19 pandemic.
Countries across the world are using digital technologies to fight this global crisis.
We show how these technologies are helping to combat this pandemic, including monitoring of the virus spread.
We discuss the challenges faced by wireless technologies, including privacy, security, and misinformation.
arXiv Detail & Related papers (2020-05-12T12:27:29Z)
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.