An integrated framework for data breach on the dark web in brand monitoring data hunting

In today's digital landscape, data breaches pose a substantial threat, with the dark web serving as a prevalent platform for malevolent actors to perpetrate such incidents. Currently, security analysts use various tools to solve the problem, which is very time-consuming. This paper introduces a novel framework that integrates data breach monitoring within the dark web, focusing on brand monitoring and data hunting. The framework starts from the scraping process and continues with the utilisation of the Splunk dashboard. The dashboard provides an exhaustive overview of data breaches related to brands for both manual inquiries and rule-based detection mechanisms. The framework comprises five phases: data sourcing, data collection, integration, monitoring, and visualisation. The visualisation phase encompasses alert generation, notification mechanisms, and reporting functionalities. Moreover, the monitoring phase provides real-time surveillance, advanced search capabilities, brand monitoring, and threat intelligence integration. The integration phase involves security information and event management (SIEM) systems and security orchestration, automation, and response (SOAR) systems. This paper's result contributes to enhancing the National Institute of Standards and Technology (NIST) cybersecurity framework, offering a comprehensive solution to the data breaches challenge within the dark web and the frontiers of knowledge and security practices.

Authors

Publication Details

Journal
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Published
2025-05-23
DOI
https://doi.org/10.11591/ijece.v15i3.pp3162-3170
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

An integrated framework for data breach on the dark web in brand monitoring data hunting

Siti Arpah Ahmad, Nor Shahniza Kamal Bashah, N.V.V.S.S. Raman, Muhammad Al’Imran Mohd Khairuddin
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Advanced Malware Detection Techniques
article

An integrated framework for data breach on the dark web in brand monitoring data hunting

Siti Arpah Ahmad, Nor Shahniza Kamal Bashah, N.V.V.S.S. Raman, Muhammad Al’Imran Mohd Khairuddin
article en

Abstract

In today's digital landscape, data breaches pose a substantial threat, with the dark web serving as a prevalent platform for malevolent actors to perpetrate such incidents. Currently, security analysts use various tools to solve the problem, which is very time-consuming. This paper introduces a novel framework that integrates data breach monitoring within the dark web, focusing on brand monitoring and data hunting. The framework starts from the scraping process and continues with the utilisation of the Splunk dashboard. The dashboard provides an exhaustive overview of data breaches related to brands for both manual inquiries and rule-based detection mechanisms. The framework comprises five phases: data sourcing, data collection, integration, monitoring, and visualisation. The visualisation phase encompasses alert generation, notification mechanisms, and reporting functionalities. Moreover, the monitoring phase provides real-time surveillance, advanced search capabilities, brand monitoring, and threat intelligence integration. The integration phase involves security information and event management (SIEM) systems and security orchestration, automation, and response (SOAR) systems. This paper's result contributes to enhancing the National Institute of Standards and Technology (NIST) cybersecurity framework, offering a comprehensive solution to the data breaches challenge within the dark web and the frontiers of knowledge and security practices.

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringVol. 15(3)
Openalex Percentile: Top 88%
Advanced Malware Detection Techniques
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