Advanced Data Pipelines for Scalable Intrusion Detection in Big Data

Abstract

In an era marked by unprecedented digital transformation, the security of information systems has become paramount. This paper explores the integration of advanced data pipelines for scalable intrusion detection in big data environments, addressing the critical challenges posed by the increasing volume and complexity of cyber threats. By leveraging cutting-edge machine learning algorithms and real-time data process capabilities, organizations can enhance their detection accuracy and response times, ultimately safeguarding their digital assets. The study highlights the importance of collaborative data sharing among organizations to create a unified defense against cyber intrusions. Through a comprehensive review of existing literature and practical applications, this research provides valuable insights into the effectiveness of advanced data pipelines in improving intrusion detection systems (IDS). As cyber threats continue to evolve, this paper serves as a crucial resource for cybersecurity professionals seeking innovative solutions to protect their networks and data.