FSCsec: Collaboration in Financial Sector Cybersecurity -- Exploring the Impact of Resource Sharing on IT Security

• URL: http://arxiv.org/abs/2410.15194v1
• Date: Sat, 19 Oct 2024 20:03:27 GMT
• Title: FSCsec: Collaboration in Financial Sector Cybersecurity -- Exploring the Impact of Resource Sharing on IT Security
• Authors: Sayed Abu Sayeed, Mir Mehedi Rahman, Samiul Alam, Naresh Kshetri,
• Abstract summary: This research aims to provide insights that can help financial institutions make better decisions to protect. By using simple theories to understand these factors, this research aims to provide insights that can help financial institutions make better decisions to protect.
• Score: 0.9374652839580183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The financial sector's dependence on digital infrastructure increases its vulnerability to cybersecurity threats, requiring strong IT security protocols with other entities. This collaboration, however, is often identified as the most vulnerable link in the chain of cybersecurity. Adopting both symbolic and substantive measures lessens the impact of IT security spending on decreasing the frequency of data security breaches in the long run. The Protection Motivation Theory clarifies actions triggered by data sharing with other organizations, and the Institutional theory aids in comprehending the intricate relationship between transparency and organizational conduct. We investigate how things like regulatory pressure, teamwork among institutions, and people's motivations to protect themselves influence cybersecurity. By using simple theories to understand these factors, this research aims to provide insights that can help financial institutions make better decisions to protect. We have also included the discussion, conclusion, and future directions in regard to collaboration in financial sector cybersecurity for exploring impact of resource sharing.

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)
• A Systematic Review of Security Communication Strategies: Guidelines and Open Challenges [47.205801464292485]
  We identify user difficulties including information overload, technical comprehension, and balancing security awareness with comfort. Our findings reveal consistent communication paradoxes: users require technical details for credibility yet struggle with jargon and need risk awareness without experiencing anxiety. This work contributes to more effective security communication practices that enable users to recognize and respond to cybersecurity threats appropriately.
  arXiv  Detail & Related papers  (2025-04-02T20:18:38Z)
• Assessing the influence of cybersecurity threats and risks on the adoption and growth of digital banking: a systematic literature review [0.0]
  This study examines the influence of cybersecurity threats on digital banking security, adoption, and regulatory compliance. It critically evaluates the most prevalent cyber threats targeting digital banking platforms, the effectiveness of modern security measures, and the role of regulatory frameworks in mitigating financial cybersecurity risks.
  arXiv  Detail & Related papers  (2025-03-23T03:14:45Z)
• 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)
• Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [46.404531555921906]
  We propose an architecture for blockchain-based PAISs aimed at preserving both confidentiality and transparency. Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.
  arXiv  Detail & Related papers  (2024-12-07T20:18:36Z)
• SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach [58.93030774141753]
  Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• Trustworthy Federated Learning: Privacy, Security, and Beyond [37.495790989584584]
  Federated Learning (FL) addresses concerns by facilitating collaborative model training across distributed data sources without transferring raw data. We conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.
  arXiv  Detail & Related papers  (2024-11-03T14:18:01Z)
• DPFedBank: Crafting a Privacy-Preserving Federated Learning Framework for Financial Institutions with Policy Pillars [0.09363323206192666]
  This paper presents DPFedBank, an innovative framework enabling financial institutions to collaboratively develop machine learning models. DPFedBank is designed to address the unique privacy and security challenges associated with financial data, allowing institutions to share insights without exposing sensitive information.
  arXiv  Detail & Related papers  (2024-10-17T16:51:56Z)
• 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)
• SeCTIS: A Framework to Secure CTI Sharing [13.251593345960265]
  The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to leaks of both proprietary and confidential data. We design a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing) to enable businesses to collaborate, preserving the privacy of their CTI data.
  arXiv  Detail & Related papers  (2024-06-20T08:34:50Z)
• Security in IS and social engineering -- an overview and state of the art [0.6345523830122166]
  The digitization of all processes and the opening to IoT devices has fostered the emergence of a new formof crime, i.e. cybercrime. The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, to the point of being perceived as the weak link'' of cybersecurity. Knowing how to anticipate, identifying weak signals and outliers, detect early and react quickly to computer crime are therefore priority issues requiring a prevention and cooperation approach.
  arXiv  Detail & Related papers  (2024-06-17T13:25:27Z)
• Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives [48.48294460952039]
  This survey offers comprehensive descriptions of the privacy, security, and fairness issues in federated learning. We contend that there exists a trade-off between privacy and fairness and between security and sharing.
  arXiv  Detail & Related papers  (2024-06-16T10:31:45Z)
• A Review of Cybersecurity Incidents in the Food and Agriculture Sector [2.0358239640633737]
  This manuscript reviews disclosed and documented cybersecurity incidents in the Food & Agriculture (FA) sector. Thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed.
  arXiv  Detail & Related papers  (2024-03-12T19:15:20Z)
• 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)
• 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)
• SMEs' Confidentiality Concerns for Security Information Sharing [1.3452510519858993]
  Small and medium-sized enterprises are considered an essential part of the EU economy, however, highly vulnerable to cyberattacks. This paper presents the results of semi-structured interviews with seven chief information security officers of SMEs to evaluate the impact of online consent communication on motivation for information sharing. The findings demonstrate that online consent with multiple options for indicating a suitable level of agreement improved motivation for information sharing.
  arXiv  Detail & Related papers  (2020-07-13T10:59:40Z)

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.