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

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 (IIDSAE-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. [ABSTRACT FROM AUTHOR]

Copyright of Revue d'Intelligence Artificielle is the property of International Information & Engineering Technology Association (IIETA) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)