Predictive Analysis on Cyber Security Threats with Key Risk Factors

Core Concepts

A machine learning-based model is developed to predict individuals who may be victims of cyber attacks by analyzing socioeconomic factors. Key risk factors are identified through association rule mining.

Abstract

The study aimed to identify and analyze the cyber-based security threats faced by Bangladeshi netizens. A questionnaire was created and data was collected from both victims and non-victims of cyber attacks. The dataset was expanded using data augmentation and feature importance was analyzed using the Chi-squared test. Backward elimination was used to select the most important 20 features.
Several machine learning classification algorithms were trained and evaluated on the dataset. The Random Forest (RF) classifier trained with 20 features achieved the highest accuracy of 95.95%. The RF classifier also had the best combination of accuracy and AUC values compared to other models.
To identify key risk factors, the selected 20 features were decomposed into 38 factors. Association rule mining using the Apriori algorithm was performed to extract rules with high confidence (above 80%) that indicate the relationship between the factors and the victim class. The top 10 association rules provide insights into the key risk factors leading to cyber attacks.

Stats

Cyber attacks cost an estimated USD 945 billion globally in 2020.
28% of organizations experienced a Ransomware attack in 2024.
Over 2 billion records were exposed due to human error in 2018.

Quotes

"Cyber risk typically refers to any risk connected to economic loss, business interruption, or deface to the prestige of an organization or individual owing to unauthorized, inappropriate, or inaccurate use of information systems."
"The critical infrastructure has become a prime target for cyber attackers, causing not only inconvenience but also life-threatening situations."
"The cost of insufficient cyber security is estimated to have reached USD 945 billion globally in 2020."

Key Insights Distilled From

A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors

by Fatama Tuz J... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00068.pdf

Deeper Inquiries

How can the predictive model's precision be further improved to enhance its effectiveness in real-world cyber threat detection

To further enhance the predictive model's precision in real-world cyber threat detection, several strategies can be implemented:

Feature Engineering: Continuously refining and selecting the most relevant features that have a significant impact on cyber risk can improve the model's accuracy.
Ensemble Methods: Utilizing ensemble methods like stacking or boosting can combine multiple models to improve predictive performance.
Hyperparameter Tuning: Fine-tuning the hyperparameters of the machine learning algorithms used in the model can optimize its performance.
Cross-Validation: Implementing cross-validation techniques can help validate the model's performance on different subsets of the data, ensuring robustness.
Anomaly Detection: Incorporating anomaly detection techniques can help identify unusual patterns in the data that may indicate potential cyber threats.
Continuous Learning: Implementing a system for continuous learning and updating the model with new data can ensure it stays relevant and effective in detecting evolving cyber threats.

What counter-arguments can be made regarding the assumption that socioeconomic factors are the primary drivers of cyber attacks

Counter-arguments against the assumption that socioeconomic factors are the primary drivers of cyber attacks include:

Technological Factors: Cyber attacks can also be driven by technological vulnerabilities, such as outdated software, weak encryption, or poor network security, rather than solely by socioeconomic factors.
Motivations: The motivations behind cyber attacks can vary widely, including political, ideological, or personal reasons, which may not always be directly linked to socioeconomic status.
Global Nature: Cyber attacks are a global phenomenon, affecting individuals and organizations across different socioeconomic backgrounds, indicating that other factors beyond socioeconomic status play a role.
Education and Awareness: Factors like cybersecurity education, awareness, and training can significantly impact an individual's susceptibility to cyber attacks, irrespective of their socioeconomic status.

How might the insights from this study on key cyber risk factors be applied to develop proactive cybersecurity strategies for vulnerable populations in developing countries

Insights from the study on key cyber risk factors can be applied to develop proactive cybersecurity strategies for vulnerable populations in developing countries in the following ways:

Education and Training: Implementing cybersecurity awareness programs tailored to the specific risk factors identified can empower vulnerable populations to recognize and mitigate cyber threats.
Resource Allocation: Allocating resources towards improving cybersecurity infrastructure, access to secure technologies, and training for at-risk communities can enhance their resilience against cyber attacks.
Collaborative Partnerships: Establishing partnerships with local organizations, governments, and international bodies to share knowledge and resources can strengthen cybersecurity measures in developing countries.
Policy Development: Advocating for policies that address the unique cyber risk factors faced by vulnerable populations, such as data privacy regulations, can provide a regulatory framework for cybersecurity protection.
Community Engagement: Engaging with communities to raise awareness, build digital literacy, and foster a culture of cybersecurity can create a more secure environment for vulnerable populations.