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Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic
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Author(s): Valliammal Narayan (Avinashilingam Institute for Home Science and Higher Education for Women, India) and Shanmugapriya D. (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2020
Pages: 30
Source title: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Source Author(s)/Editor(s): Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India) and D. Shanmugapriya (Avinashilingam Institute for Home Science and Higher Education for Women, India)
DOI: 10.4018/978-1-5225-9611-0.ch015

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Abstract

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.

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