Effective Intrusion Detection System Using XGBoost

By Sukhpreet Singh Dhaliwal, A. Nahid, Robert Abbas. Inf., 2018

430 citations recorded by the source corpus.

As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains “security”, as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cde4c52d2526ebfae72c8428641f88445d50ef02

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Citations in the corpus

Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, methodology): “Then in the fine-grained layer, gcForest (caXGBoost) is used to further classifies the abnormal classes into N -1 subclasses.”
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, background): “XGBoost as one of the flexible models has some advantages, such as it can work on both regression and classification problems, parallel processing, and it can work effectively with large and multidimensional datasets [8, 98, 99].”