Abstract
The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
Highlights
Life without internet has almost become impossible in the present day and age
The evolution of XGBoost algorithm started with the decision tree based approach wherein graphical representations of possible solutions for a decision is computed depending on certain conditions
The hard disk provided by Google platform is provided by the GPU platform has helped in the analysis of multiple inputs simultaneously from the
Summary
Life without internet has almost become impossible in the present day and age. The potential of internet is enormous and its growth has been reflected in the functioning of business models pertaining to education, entertainment, healthcare and all the various types of organizations around the world. The process of identifying the different types of intrusions in a network is performed by an intrusion detection system (IDS) [1]. The evolution of XGBoost algorithm started with the decision tree based approach wherein graphical representations of possible solutions for a decision is computed depending on certain conditions. An ensemble meta algorithm aggregating predictions from various decision trees based on majoritarian voting technique was created named ‘bagging’. This bagging approach further evolved to construct a forest or aggregation of decision trees by randomly selecting features. As a further improvement the gradient decent algorithm was employed to reduce the errors in the sequential model.
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