Defending against cybersecurity threats to the payments and banking
system
- URL: http://arxiv.org/abs/2212.12307v1
- Date: Thu, 15 Dec 2022 11:55:11 GMT
- Title: Defending against cybersecurity threats to the payments and banking
system
- Authors: Williams Haruna and Toyin Ajiboro Aremu and Yetunde Ajao Modupe
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cyber security threats to the payment and banking system have become a
worldwide menace. The phenomenon has forced financial institutions to take
risks as part of their business model. Hence, deliberate investment in
sophisticated technologies and security measures has become imperative to
safeguard against heavy financial losses and information breaches that may
occur due to cyber-attacks. The proliferation of cyber crimes is a huge concern
for various stakeholders in the banking sector. Usually, cyber-attacks are
carried out via software systems running on a computing system in cyberspace.
As such, to prevent risks of cyber-attacks on software systems, entities
operating within cyberspace must be identified and the threats to the
application security isolated after analyzing the vulnerabilities and
developing defense mechanisms. 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. Thus,
adopting the right application to the security threats and defenses will aid IT
professionals and users alike in making decisions for developing a strong
defense-in-depth mechanism.
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) - Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures [0.0]
Cyber threats pose risks at individual, organizational, and societal levels.
This paper proposes a comprehensive cybersecurity strategy that integrates AI-driven solutions with targeted policy interventions.
arXiv Detail & Related papers (2025-01-03T09:26:50Z) - 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 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) - 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) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z) - A Framework for Evaluating the Cybersecurity Risk of Real World, Machine
Learning Production Systems [41.470634460215564]
We develop an extension to the MulVAL attack graph generation and analysis framework to incorporate cyberattacks on ML production systems.
Using the proposed extension, security practitioners can apply attack graph analysis methods in environments that include ML components.
arXiv Detail & Related papers (2021-07-05T05:58:11Z) - A System for Efficiently Hunting for Cyber Threats in Computer Systems
Using Threat Intelligence [78.23170229258162]
We build ThreatRaptor, a system that facilitates cyber threat hunting in computer systems using OSCTI.
ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, and (3) a query synthesis mechanism that automatically synthesizes a TBQL query from the extracted threat behaviors.
arXiv Detail & Related papers (2021-01-17T19:44:09Z) - Review: Deep Learning Methods for Cybersecurity and Intrusion Detection
Systems [6.459380657702644]
Artificial Intelligence (AI) and Machine Learning (ML) can be leveraged as key enabling technologies for cyber-defense.
In this paper, we are concerned with the investigation of the various deep learning techniques employed for network intrusion detection.
arXiv Detail & Related papers (2020-12-04T23:09:35Z) - 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.