Detection of Cyber Attacks with Machine Learning Algorithms

Cyber-attack types and application areas have diversified considerably in the present era. The diversification of cyberattacks has led to a continuous increase in their effects. Consequently, detecting cyberattacks has become a crucial aspect of cybersecurity. Attacks such as Brute Force, HTTP DDoS, ICMP Flood, Port Scan, and Web Crawling have gained significant prominence in the cyber domain. The utilization of diverse techniques and methods has led to challenges in the detection of these attack types, which have, in turn, undermined the comprehensive security of cyber systems.This study employed the MSCAD data set, a frequently used data set in intrusion detection systems taken from the Kaggle platform. Initially, the data set underwent a series of preprocessing steps, including data cleaning, removing low-variance features, and eliminating missing values. Subsequently, the pre-processed data set was analyzed using machine learning algorithms, including Random Forest, Decision Tree, Neural Network, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). Furthermore, the chi-square feature selection algorithm was applied to the pre-processed dataset, minimizing the number of features. Subsequently, the same machine learning algorithms were applied to the dataset and analyzed. This study conducted experiments on Orange3 and JupyterLab, and the results were compared. With the successful results obtained through machine learning algorithms, improvements can be made to Intrusion Detection Systems.