https://journal.50sea.com/index.php/IJIST/article/view/645/1249
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 1
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 the resilience of businesses against cyber threats. Our methodology employs the Kafka tool and Spark for real-time identification of ransomware exploits. The research utilizes the CICMalMem-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.
Authors and Affiliations
Saad Khan, Rana Marwat Hussain, Talha Saleem Baig, Mian Muhammad Qasim
Keywords
Related Articles
- EP ID EP760295
- DOI -
- Views 22
- Downloads 0
Deep Learning-Based Automated Classroom Slide Extraction
Automated extraction of valuable content from real-time classroom lectures holds significant potential for enhancing educational accessibility and efficiency. However, capturing the spontaneous insights of live lecture...
Lower Limb Exo-Skeleton for Rehabilitation
Above-knee amputation remains a significant global issue, leaving many people physically disabled due to various natural and man-made causes, such as diseases, wars, and disasters. This article presents a novel, non-in...
Predictive Maintenance in Industrial Internet of Things: Current Status
Introduction/Importance of Study: Predictive Maintenance (PdM) is a key challenge within the Industrial Internet of Things (IIoT). It aims to enhance system operations by minimizing equipment failures, leading to smoot...
Framework for Modeling Risk Factors in Green Agile Software Development for GSD Vendors
In the last decades, agile methodologies are commonly employed to develop and deliver valuable software, with high user satisfaction at a comparatively low cost. However in recent years, the emergence of Green Software...
An IoT Distributive SM Controller for Mitigation of Circulating Currents Among Sources in a Standalone DC Microgrid
Sources of similar or different power ratings are connected in parallel within the DC microgrid. During operation, these sources generate circulating currents along with their normal currents, which disrup...