DEEP LEARNING METHOD FOR INTRUSION DETECTION IN NETWORK SECURITY
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Abstract
Nowadays, large numbers of people were affected by data infringes and cyber-attacks due to dependency on internet. India is lager country for any resource use or consumer. Over the past ten years, the average cost of a data breach has increased by 12%. Hacking in India is take share of 2.3% of global criminal activity. To prevent such malicious activity, the network requires a system that detects anomaly and inform to the admin or service operator for taking an action according to the alert. System used for intrusion detection (IDS) is software that helps to identify and observes a network or systems for malicious, anomaly or policy violation. Deep learning algorithm techniques is an advanced method for detect intrusion in network. In this paper, intrusion detection model is train and test by NSL-KDD dataset which is enhanced version of KDD99 dataset. Proposed method operations are done by Long Short-Term Memory (LSTM) and detect attack. So admin can take action according to alert for prevent such activity. This method is used for binary and multiclass classification of data for binary classification it gives 99.2% accuracy and for multiclass classification it gives 96.9% accuracy.
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