A Comparative Study of Cyber Attack Detection & Prediction Using Machine Learning Algorithms
Swati Prakash Gawand, Meesala Sudhir Kumar · Research Square · 2023
Abstract Nowadays, Internet utilization is expanding rapidly. This is primarily attributable to the recent tendency of cheaper electronic devices and data packages. In reality, the Internet is a vital part of people's daily lives in the modern world. Due to the high usage, our confidential information may be accidentally leaked elsewhere on the internet. Cyber security is a broad area of study that addresses the fundamentals of data misuse and internet security threats. Cyber security is crucial in every industry to protect corporate data and to comprehend assault vectors. Protecting information from Cyber-attacks has become more challenging. In this paper we present a proposed system that analyses the dataset and identifies whether given data is normal or anomalous we use machine learning algorithms to analyze datasets in the proposed system. After analyzing the dataset, attempt to identify and predict a cyber-attack. For classification, detection, and prediction of cyber-attacks, algorithms such as K Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Gradient Boosting Classifier (GB) and Random Forest (RF) are used. Here we are using a dataset containing a wide variety of simulated intrusions into a military network environment that was made available. This paper compares machine learning algorithms based on parameters such as Precision, Recall, F1 Score, Accuracy, and Cross-Validation of the system and suggests which algorithm provides accurate results. Publicly available datasets (Network intrusion dataset) were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection