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.
Highlights
One of the most important needs in life is security, whether in normal day-to-day life or in the cloud world
This paper provides results which tell us that XGBoost is very well suited to build up a strong classification model
There is something called True Negatives (TN), which refers to Moving further, the Recall parameter was calculated by dividing the number of True Positives (TP) by the total correct prediction of data being an anomaly
Summary
One of the most important needs in life is security, whether in normal day-to-day life or in the cloud world. The VM introspection can be done in various ways such as kernel debugging, interrupt based, hyper-call authentication based, guest-OS hook based, and VM state access based These all ways help to determine whether any suspicious programs are running at the low-level or high-level semantic end of the VM. HVI (Hyper-Visor Introspection) is developed; this depends mainly on hardware involvement to check the kernel states of the host and hypervisor operating system. Attacks such as rootkit, side-channel attacks, and hardware attacks can take place.
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