Network  Intrusion  Detection  and classification using Stacked Autoencoder with Random Forest Technique

Girum, Berhanu  Yireda

dc.contributor.author	Girum, Berhanu Yireda
dc.date.accessioned	2022-11-16T11:04:11Z
dc.date.available	2022-11-16T11:04:11Z
dc.date.issued	2022-07-30
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14392
dc.description.abstract	Compared to other conventional network threat prevention technologies, such as antivirus and firewalls, network intrusion detection models (NIDS) offer a better option for network intrusion detection. For intrusion detection, numerous machine learning algorithms have been developed in the past. Dimensionality reduction-based techniques utilize the reconstruction error. PCA is a well-known technique for dimensionality reduction. Consequently, it has been utilized in recent articles for network intrusion detection. However, principal component analysis (PCA) is a method for avoiding information loss while boosting interpretability by lowering the dimensionality of such datasets. It accomplishes this by producing new, uncorrelated variables that maximize variance one after the other. may result in several false reports as it can only identify a linear association between features. But the majority of the features in network intrusion detection are not linearly correlated. It has been confirmed that autoencoders are a more effective dimensionality reduction technique than PCA for non-linearly correlated features. In this research work, we propose an autoencoder-based approach for dimensionality reduction. It constructively improves the prediction accuracy of a random forest classifier (RF). The stacked autoencoder mechanism (SAE), an effective learning approach for building a new feature representation unsupervised, is used to build the proposed model. The new attributes are added to the RF algorithm after the pre-training step to boost the algorithm's classification precision and intrusion detection ability. Additionally, the approach's effectiveness in binary and multiclass classification is evaluated in comparison to that of a single RF classifier. The NSL-KDD dataset, which includes normal and attack data, is used in the experimental work. The result shows that the SAE-RF approach scores better result than that of a single RF. SAE-RF scores 99% accuracy,99.48% F1-score,99.5 precision, and 99.48% recall for binary class classification based on training data. KeyWords:- Network intrusion detection , NSL-KDD , SAE-RF, RF ,PCA	en_US
dc.language.iso	en_US	en_US
dc.subject	FACULTY OF COMPUTING	en_US
dc.title	Network Intrusion Detection and classification using Stacked Autoencoder with Random Forest Technique	en_US
dc.type	Thesis	en_US