The Inner Workings of Windows Security
- URL: http://arxiv.org/abs/2312.15150v1
- Date: Sat, 23 Dec 2023 03:35:57 GMT
- Title: The Inner Workings of Windows Security
- Authors: Ashvini A Kulshrestha, Guanqun Song, Ting Zhu,
- Abstract summary: The year 2022 saw a significant increase in Microsoft vulnerabilities, reaching an all-time high in the past decade.
This project aims to investigate the vulnerabilities of the Windows Operating System and explore the effectiveness of key security features.
Based on the results, this study will provide recommendations for mitigation strategies to enhance system security and strengthen the protection provided by Windows security features.
- Score: 4.424739166856966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The year 2022 saw a significant increase in Microsoft vulnerabilities, reaching an all-time high in the past decade. With new vulnerabilities constantly emerging, there is an urgent need for proactive approaches to harden systems and protect them from potential cyber threats. This project aims to investigate the vulnerabilities of the Windows Operating System and explore the effectiveness of key security features such as BitLocker, Microsoft Defender, and Windows Firewall in addressing these threats. To achieve this, various security threats are simulated in controlled environments using coded examples, allowing for a thorough evaluation of the security solutions' effectiveness. Based on the results, this study will provide recommendations for mitigation strategies to enhance system security and strengthen the protection provided by Windows security features. By identifying potential weaknesses and areas of improvement in the Windows security infrastructure, this project will contribute to the development of more robust and resilient security solutions that can better safeguard systems against emerging cyber threats.
Related papers
- 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) - Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC)
ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic.
We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO)
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
arXiv Detail & Related papers (2024-11-21T08:20:31Z) - 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) - Enhancing cybersecurity defenses: a multicriteria decision-making approach to MITRE ATT&CK mitigation strategy [0.0]
This paper proposes a defense strategy for the presented security threats by determining and prioritizing which security control to put in place.
This approach helps organizations achieve a more robust and resilient cybersecurity posture.
arXiv Detail & Related papers (2024-07-27T09:47:26Z) - The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration [0.0]
Stealth Data Exfiltration is a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data.
Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats.
As we navigate this complex landscape, it is crucial to anticipate potential threats and continually update our defenses.
arXiv Detail & Related papers (2024-05-17T16:14:45Z) - Secure Software Development: Issues and Challenges [0.0]
The digitization of our lives proves to solve our human problems as well as improve quality of life.
Hackers aim to steal the data of innocent people to use it for other causes such as identity fraud, scams and many more.
The goal of a secured system software is to prevent such exploitations from ever happening by conducting a system life cycle.
arXiv Detail & Related papers (2023-11-18T09:44:48Z) - 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) - Defending against cybersecurity threats to the payments and banking
system [0.0]
The proliferation of cyber crimes is a huge concern for various stakeholders in the banking sector.
To prevent risks of cyber-attacks on software systems, entities operating within cyberspace must be identified.
This paper will examine various approaches that identify assets in cyberspace, classify the cyber threats, provide security defenses and map security measures to control types and functionalities.
arXiv Detail & Related papers (2022-12-15T11:55:11Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - 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.