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

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.