An Efficient Anomaly Detection Based On Optimal Deep Belief Network in Big Data

Abstract

Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

Theoretical Perspectives of Nursing Education

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