Ransomware Resilience: A Real-Time Detection Framework using Kafka and Machine Learning

Saad Khan ()
Additional contact information
Saad Khan: School of Systems and Technology, University of the Management and Technology, Lahore, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 1, 70-82

Abstract: Ransomware has emerged as a prominent cyber threat in recent years, targeting numerous businesses. In response to the escalating frequency of attacks, organizations are increasingly seeking effective tools and strategies to mitigate the impact of ransomware incidents. This research addresses the pressing need for real-time detection of ransomware, offering a solution that leverages cutting-edge technologies. The surge in ransomware attacks poses a significant challenge to the cybersecurity landscape, compelling organizations to adopt proactive measures. Recognizing the urgency of the situation, this study motivates the exploration of an innovative approach to ransomware detection. By utilizing advanced tools such as Apache Kafka and Spark, we aim to enhance detection capabilities and contribute to theresilience of businesses against cyber threats. Our methodology employs the Kafka tool and Spark for real-time identification of ransomware exploits. The research utilizes the CIC-MalMem-2022 dataset to develop and validate the proposed model. The integration of Apache Kafka with traditional machine learning techniques is explored to improve the accuracy of cyber threat detection, offering a comprehensive and efficient solution. The implemented model exhibits a commendable detection rate of 95.2%, demonstrating its effectiveness in identifying ransomware attacks in real-time. The combination of Apache Kafka's streaming capabilities and established machine learning methodologies proves to be a potent defense against the evolving landscape of cyber threats. In conclusion, our research provides a robust and practical approach to combating ransomware threats through real-time detection. By leveraging the synergy of Kafka and machine learning, organizations can fortify their cybersecurity defenses and respond proactively to potential ransomware exploits. This study contributes valuable insights and tools to the ongoing efforts in enhancing cyber resilience.

Keywords: Ransomware; Machine Learning; Real Time; Kafka (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/651/1251 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/651 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:1:p:70-82

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().