Implementation of ransomware prediction system based on weighted-KNN and real-time isolation architecture on SDN Networks

Chang, Hong-Yi; Lin, Tu-Liang; Hsu, Tzu-Fan; Shen, Yu-Shiang; Li, Guanrong

doi:10.1109/icce-tw46550.2019.8991771

Cited by 3 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support Vector Machine has been found to be more efficient when compared with other machine learning classifiers. Chang et al [145] implemented a prediction system that adopts a k-nearest neighbor algorithm to detect and predict ransomware network traffic. By employing a static analysis approach, the system monitors unknown IP traffic that is an indication of malicious activity.…”

Section: E Predictionmentioning

confidence: 99%

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

et al. 2023

View full text Add to dashboard Cite

The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputational, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized machine learning methods. We further discuss the challenges found such as the ones related to obtaining ransomware datasets. In addition, our study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware. Adversarial machine learning exploitation has been identified as an under-researched area in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.

show abstract

Section: E Predictionmentioning

confidence: 99%

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Ransomware was brought back into the limelight in 2017 with the broad and well reported WannaCry outbreak (Chang et al, 2019). This assault highlighted the magnitude of ransomware profitability as well as its potential destruction.…”

Section: Introductionmentioning

confidence: 99%

Fortifying Against Ransomware: Navigating Cybersecurity Risk Management with a Focus on Ransomware Insurance Strategies

Rehman Shaikh,

Ullah,

Akbar

et al. 2024

IJARBSS

View full text Add to dashboard Cite

This study explores the issue of preventing ransomware attacks using risk management and insurance techniques. Threats from ransomware, which compromise operations, data integrity, and financial stability, have emerged as a major concern for enterprises. The present status of ransomware attacks and their possible effects on organizations are first examined after which we analyse several strategies for reducing the dangers to cyber security posed by these threats. These include non-technical approaches such as conducting a risk assessment to identify areas of vulnerability, implementing a comprehensive cybersecurity policy, obtaining appropriate cybersecurity insurance coverage, and technical measures such as firewall protection, user education, and software vulnerability patching. Such tactics rely heavily on ransomware insurance, which provides monetary security and assistance for incident responses. The findings show that, while technological solutions are essential for efficient ransomware attack mitigation, they should be accompanied by strong insurance plans created to offer financial security in the event of an attack using static and dynamic analysis detection techniques. This article provides a thorough overview of the complexities of ransomware insurance strategies by synthesizing opinions from industry experts, legal viewpoints, and cybersecurity professionals. To reduce their exposure to potential crippling losses due to successful breaches, organizations must take proactive steps to defend themselves against the constantly evolving threat of ransomware by utilizing both technical and non-technical measures, including adequate cyber security insurance with machine learning techniques.

show abstract

SDN as a defence mechanism: a comprehensive survey

Ayodele,

Buttigieg

2023

Int. J. Inf. Secur.

View full text Add to dashboard Cite

Investing in cybersecurity is increasingly considered a significant area and aspect a business or organisation should seriously consider. Some of these security solutions are network-based and provide many levels of protection. However, traditional networks are seen to be vendor-specific and are limited, enabling minor to no network flexibility or customisation. Implementing SDN to combat cyberattacks is a workable option for resolving this traditional network constraint. Less attention has been paid to how SDN has been utilised to address security concerns, with most surveys concentrating on the security challenges the SDN paradigm faces. This study aims to provide a comprehensive overview of the state-of-the-art on how SDN has been used to combat attacks between 2017 and 2022 by highlighting the specifics of each literature, its advantages, limitations, and potential areas for further study. This work introduces a taxonomy highlighting SDN’s fundamental traits and contributions as a defence mechanism (SaaDM).

show abstract

Implementation of ransomware prediction system based on weighted-KNN and real-time isolation architecture on SDN Networks

Cited by 3 publications

References 2 publications

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions

Fortifying Against Ransomware: Navigating Cybersecurity Risk Management with a Focus on Ransomware Insurance Strategies

SDN as a defence mechanism: a comprehensive survey

Contact Info

Product

Resources

About