Effective_Intrusion_Detection_System_Using_XGBoost.pdf - information Article Effective Intrusion Detection System Using XGBoost Sukhpreet Singh Dhaliwal

Effective_Intrusion_Detection_System_Using_XGBoost.pdf -...

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information Article Effective Intrusion Detection System Using XGBoost Sukhpreet Singh Dhaliwal * ID , Abdullah-Al Nahid ID and Robert Abbas ID School of Engineering, Macquarie University, Sydney NSW 2109, Australia; [email protected] (A.-A.N.); [email protected] (R.A.) * Correspondence: [email protected]; Tel.: +61-2-9850-1558 Received: 21 May 2018; Accepted: 19 June 2018; Published: 21 June 2018 Abstract: 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. Keywords: classifiers; eXtreme Gradient Boosting (XGBoost); intrusion detection system (IDS); network socket layer-knowledge discovery in databases (NSL-KDD) 1. Introduction One of the most important needs in life is security, whether in normal day-to-day life or in the cloud world. The year 2017 witnessed a series of ransomware attacks (a simple form of malware that locks down computer files using strong encryption, and then hackers ask for money in exchange for release of the compromised files), targets including San Francisco’s light-rail network, Britain’s National Health Service, and even companies such as FedEx. One example is the WannaCry Ransomware Attack which compromised thousands of computers, and lately companies such as Amazon, Google, and IBM have started hiring the best minds in digital security so that their establishments across the world do not get easily compromised. Moreover, one can ask Amazon, Twitter, Netflix, and others about the Denial of Service attacks their servers faced back in 2016 [ 1 ], in which the attackers flooded the system with useless packets, making the system unavailable. There were virtual machine escape attacks reported back in 2008 by Core Security Technologies, in which a vulnerability (CVE-20080923) was found in VMware’s (software developing firm named VMware Inc., Palo Alto, CA, USA) mechanism of shared
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