BIG DATA-DRIVEN CYBERSECURITY: INTELLIGENT MODELS FOR EARLY THREAT IDENTIFICATION AND MITIGATION

Atif Iftikhar; Farhana Batool; Asma Javaid; Dr. Alamgir Safi

Authors

Atif Iftikhar
Farhana Batool
Asma Javaid
Dr. Alamgir Safi

Keywords:

Big Data Analytics, Cybersecurity, Early Threat Identification, Threat Mitigation, Data-Driven Security, Predictive Monitoring

Abstract

Background

Contemporary companies deal with the emerging cyber threats that are too fast, too sophisticated and quite irritating to be dealt with by the old-fashioned security tools. The big data analytics provides a more flexible and proactive method whereby, large, high-velocity security data are analyzed to detect the early signs of an attack. This paper will focus on the efficacy of big data-based paradigms in the detection and alleviation of threats at an initial stage within organizational cybersecurity.

Methods

A designed survey was executed on a simulated sample of 250 participants in different organizational functions. The tool measured the five areas which are Awareness, Early Threat Identification, Mitigation and Response, Challenges and Overall Effectiveness. The dataset was analyzed using descriptive statistics, reliability analysis, correlation analysis, frequency distribution and OLS regression.

Results

All the items on the scale were highly reliable with the alpha-coefficients of Cronbach being 0.759 to 0.888. The descriptive results indicated that the level of awareness and the adoption of big data tools were moderate (Mean = 3.00, SD = 1.42). There were positive correlations between Awareness, Early Threat Identification, Mitigation and Overall Effectiveness. Regression analysis revealed that Awareness ( = 0.367, p = 0.001), Early Threat Identification ( = 0.179, p = 0.003) and Mitigation ( = 0.318, p = 0.001) had significant predictive value on overall cybersecurity effectiveness. Challenges were not found to be statistically significant. The model had explained 49.5% of the variance in effectiveness (Adjusted R 2 = 0.487).

Conclusion

Results indicate that big data analytics can greatly enhance early threat detection and mitigation, which eventually enhances performance on cybersecurity. Companies investing in data-driven security solutions and skills acquisition have received quantifiable improvement in responsiveness, accuracy, and prediction. Their disadvantages, including cost and inexperience, are still present, but the overall effectiveness of the big data-driven cybersecurity systems is not overshadowed by it.