The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration
- URL: http://arxiv.org/abs/2405.10880v1
- Date: Fri, 17 May 2024 16:14:45 GMT
- Title: The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration
- Authors: Sanjeev Pratap Singh, Naveed Afzal,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration, 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, highlighting the immediate need for a shift in information risk management across businesses. The evolving nature of cyber threats, driven by advancements in techniques such as social engineering, multi-vector attacks, and Generative AI, underscores the need for robust, adaptable, and comprehensive security strategies. As we navigate this complex landscape, it is crucial to anticipate potential threats and continually update our defenses. We propose a shift from traditional perimeter-based, prevention-focused models, which depend on a static attack surface, to a more dynamic framework that prepares for inevitable breaches. This suggested model, known as MESA 2.0 Security Model, prioritizes swift detection, immediate response, and ongoing resilience, thereby enhancing an organizations ability to promptly identify and neutralize threats, significantly reducing the consequences of security breaches. This study suggests that businesses adopt a forward-thinking and adaptable approach to security management to stay ahead of the ever-changing cyber threat landscape.
Related papers
- Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey [25.73174314007904]
Traditional cybersecurity approaches are struggling to adapt to the rapidly evolving nature of modern cyberattacks.
The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats.
exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field.
arXiv Detail & Related papers (2025-04-22T06:28:08Z) - An Approach to Technical AGI Safety and Security [72.83728459135101]
We develop an approach to address the risk of harms consequential enough to significantly harm humanity.
We focus on technical approaches to misuse and misalignment.
We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
arXiv Detail & Related papers (2025-04-02T15:59:31Z) - Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence [0.0]
This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity.
We examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem.
Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
arXiv Detail & Related papers (2025-02-28T20:23:35Z) - Comprehensive Digital Forensics and Risk Mitigation Strategy for Modern Enterprises [0.0]
This study outlines an approach to cybersecurity, including proactive threat anticipation, forensic investigations, and compliance with regulations like CCPA.
Key threats such as social engineering, insider risks, phishing, and ransomware are examined, along with mitigation strategies leveraging AI and machine learning.
The findings emphasize the importance of continuous monitoring, policy enforcement, and adaptive security measures to protect sensitive data.
arXiv Detail & Related papers (2025-02-26T23:18:49Z) - 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) - 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) - Principles of Designing Robust Remote Face Anti-Spoofing Systems [60.05766968805833]
This paper sheds light on the vulnerabilities of state-of-the-art face anti-spoofing methods against digital attacks.
It presents a comprehensive taxonomy of common threats encountered in face anti-spoofing systems.
arXiv Detail & Related papers (2024-06-06T02:05:35Z) - Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective [53.24281798458074]
Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication.
Recent studies have revealed vulnerabilities in FRS to adversarial (e.g., adversarial patch attacks) and backdoor attacks (e.g., training data poisoning)
arXiv Detail & Related papers (2024-05-21T13:34:23Z) - Generative AI in Cybersecurity [0.0]
Generative Artificial Intelligence (GAI) has been pivotal in reshaping the field of data analysis, pattern recognition, and decision-making processes.
As GAI rapidly progresses, it outstrips the current pace of cybersecurity protocols and regulatory frameworks.
The study highlights the critical need for organizations to proactively identify and develop more complex defensive strategies to counter the sophisticated employment of GAI in malware creation.
arXiv Detail & Related papers (2024-05-02T19:03:11Z) - 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) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z) - 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) - Reinforcement Learning for Feedback-Enabled Cyber Resilience [24.92055101652206]
Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms.
A Cyber-Resilient Mechanism ( CRM) adapts to the known or zero-day threats and uncertainties in real-time.
We review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities.
arXiv Detail & Related papers (2021-07-02T01:08:45Z) - A System for Automated Open-Source Threat Intelligence Gathering and
Management [53.65687495231605]
SecurityKG is a system for automated OSCTI gathering and management.
It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
arXiv Detail & Related papers (2021-01-19T18:31:35Z) - 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.