Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

Yadala Prabhu Kumar; Burra Vijaya Babu

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Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

机译：Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

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Using an Intelligent Intrusion Detection System (IIDS) instead of less effective firewalls and other intrusion detection systems can increase network security. The system's overall effectiveness is determined by the accuracy and speed of IIDS' categorization and training algorithms. According to research, Stabbing Intrusion Using Learning Framework (SILF) is an innovative and intelligent method of learning attack features and lowering dimensionality. To improve Enhanced Long Short-Term Memory (ELSTM) prediction accuracy while minimising testing and training time, an auto-encoder approach, which is an efficient learning methodology for feature generation in an unsupervised way is applied. Initial training samples are fed into the classifier to increase the predictability of incursion and classification accuracy. Thus, model efficacy may be achieved linearly while alternative classifier approaches such as conventional SVM, Random Forest (RF), and Naive Bayesian (NB) are investigated and compared. In this research, an efficient Intelligent Intrusion Detection System using Auto Encoder with Enhanced LSTM (IIDS-AE-ELSTM) is proposed for feature dimensionality reduction. Testing and training have shown that the proposed model is more efficient than existing systems in terms of performance measures such as accuracy, precision, recall, and F-measure. A new method to intrusion detection is presented, which increases detection of network intrusions with dimensionality reduction. The Python environment is used in the proposed model to create an efficient dimensionality reduction model for intrusion detection.

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来源
《Revue d'intelligence artificielle》 |2022年第5期|737-743|共7页
作者
Yadala Prabhu Kumar; Burra Vijaya Babu;
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作者单位

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation;

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原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
Network security; Intrusion detection; Learning framework; Linear support vector machine; Auto encoder; Intellectual model;

Stabbing of Intrusion with Learning Framework Using Auto Encoder Based Intellectual Enhanced Linear Support Vector Machine for Feature Dimensionality Reduction

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