Enhancing Big Data Security in Hadoop using Machine Learning - Mr. S.B. Khandagale; Dr. Bhavana Narain; Dr. B. T. Jadhav (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
In the era of Big Data, where vast amounts of information are generated and analysed to extract valuable insights, ensuring the security of data has become paramount. Hadoop, as a prominent framework for processing and analysing Big Data, presents unique challenges in terms of security due to its distributed and decentralized architecture. Traditional security mechanisms in Hadoop, such as authentication, authorization, and encryption, are essential but may not suffice to address evolving security threats effectively. This research paper proposes an innovative approach to enhance Big Data security in Hadoop using Machine Learning techniques. Machine Learning offers the capability to detect anomalies, identify patterns, and classify data, which can complement traditional security measures and provide proactive defence mechanisms against sophisticated attacks. The literature review highlights the limitations of existing security mechanisms in Hadoop and discusses the potential of Machine Learning in addressing these challenges. Various Machine learning algorithms, including anomaly detection, pattern recognition, and classification, are explored for their applicability in Big Data security. The proposed methodology involves integrating Machine Learning algorithms into the Hadoop ecosystem to analyse data access patterns, detect abnormal behaviour, and identify potential security breaches in real-time. The experimental setup comprises the selection of relevant datasets, implementation details using appropriate tools and frameworks, and evaluation using established metrics. Results from experiments demonstrate the effectiveness of the proposed approach in enhancing Big Data security in Hadoop. By leveraging Machine Learning, organizations can improve their ability to detect and mitigate security threats, thereby safeguarding sensitive data and preserving the integrity of their Big Data infrastructure. The discussion section interprets the findings in the context of existing literature, highlighting the significance of the research and identifying avenues for further exploration. Ultimately, this research contributes to the advancement of Big Data security practices by leveraging Machine Learning techniques to fortify the defences of Hadoop-based systems against evolving cyber threats.