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Big Data in Real Time to Detect Anomalies
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Author(s): N. Abinaya (Hindusthan College of Arts and Sciences, India), A. V. Senthil Kumar (Hindusthan College of Arts and Sciences, India), Ankita Chaturvedi (IIS University (Deemed), India), Ismail Bin Musirin (Universiti Teknologi Mara, Malaysia), Manjunatha Rao (National Assessment and Accreditation Council, India), Gaganpreet Kaur (Chitkara University Institute of Engineering and Technology, Chitkara University, India), Sarabjeet Kaur (Zakir Husain Delhi College Evening, India), Omar S. Saleh (Studies, Planning, and Followup Directorate, Iraq), Ravisankar Malladi (Koneru Lakshmaiah Education Foundation, India)and Nitin Arya (India Para Power Lifting, India)
Copyright: 2024
Pages: 26
Source title:
Big Data Analytics Techniques for Market Intelligence
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)
DOI: 10.4018/979-8-3693-0413-6.ch015
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Abstract
The proliferation of linked devices and the Internet have made it easier for hackers to infiltrate networks, which can result in cyber attacks, financial loss, healthcare information theft, and cyber war. As a result, network security analytics has drawn a lot of interest from researchers lately, especially in the field of anomaly detection in networks, which is seen to be essential for network security. Current methods are ineffective mostly because of the large amounts of data that linked devices have amassed. It is essential to provide a framework that can manage real-time massive data processing and identify network irregularities. This study makes an effort to solve the problem of real-time anomaly detection. This work has examined both the key features of related machine learning algorithms and the most recent real-time big data processing technologies for anomaly detection. The recognized research problems of massive data processing in real-time for anomaly detection are described at this point.
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