Abstract
The growth of cloud in modern technology is drastic by provisioning services to various industries where data security is considered to be common issue that influences the intrusion detection system (IDS). IDS are considered as an essential factor to fulfill security requirements. Recently, there are diverse Machine Learning (ML) approaches that are used for modeling effectual IDS. Most IDS are based on ML techniques and categorized as supervised and unsupervised. However, IDS with supervised learning is based on labeled data. This is considered as a common drawback and it fails to identify the attack patterns. Similarly, unsupervised learning fails to provide satisfactory outcomes. Therefore, this work concentrates on semi-supervised learning model known as Fuzzy based semi-supervised approach through Latent Dirichlet Allocation (F-LDA) for intrusion detection in cloud system. This helps to resolve the aforementioned challenges. Initially, LDA gives better generalization ability for training the labeled data. Similarly, to handle the unlabelled data, Fuzzy model has been adopted for analyzing the dataset. Here, pre-processing has been carried out to eliminate data redundancy over network dataset. In order to validate the efficiency of F-LDA towards ID, this model is tested under NSL-KDD cup dataset is a common traffic dataset. Simulation is done in MATLAB environment and gives better accuracy while comparing with benchmark standard dataset. The proposed F-LDA gives better accuracy and promising outcomes than the prevailing approaches.
Highlights
With today’s technological development, Network Intrusion Detection System (NIDS) is a software or device that predicts abnormal functionality of network system by analyzing and monitoring network [1]
This work anticipates a novel supervised learning approach (SSLA) through hybridization of Fuzzy based semi-supervised approach through Latent Dirichlet Allocation (F-LDA) for intrusion detection in cloud system
The labeled data has to correct the unlabeled data. This is by means of lacking in labeled data; unlabeled data utilization performs classifier model construction to perform detection procedure for robustness and accuracy. This is work is structured as trails: Section 2 offers diverse background study based on Machine Learning (ML) based techniques for NIDS and brief explanation towards SSLA
Summary
With today’s technological development, Network Intrusion Detection System (NIDS) is a software or device that predicts abnormal functionality of network system by analyzing and monitoring network [1]. Of SLA, the ULA train detection approach devoid of any labeled instances and determines hidden unlabelled data structure. This work anticipates a novel SSLA through hybridization of Fuzzy based semi-supervised approach through Latent Dirichlet Allocation (F-LDA) for intrusion detection in cloud system. This approach reduces the variance in classifier outputs. This is by means of lacking in labeled data; unlabeled data utilization performs classifier model construction to perform detection procedure for robustness and accuracy This is work is structured as trails: Section 2 offers diverse background study based on ML based techniques for NIDS and brief explanation towards SSLA.
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