A Machine Learning Approach for Detecting Network Threats

In the context of the rapid expansion of computer networks and the increasing reliance on digital communication, ensuring the security and integrity of network systems has become a paramount concern. Detecting network threats play a vital role in safeguarding networks by identifying potential malicious activities and unauthorized access attempts. In this research paper, we present a comprehensive study that explores the efficacy of different machine learning classifiers for network threat detection. The study utilizes a publicly available dataset containing network traffic data, encompassing various network protocols and attack. The dataset is pre-processed to convert categorical variables into numerical form using one-hot encoding. Four popular classifiers are employed in this study: Support Vector Machine (SVM), Decision Tree Classifier (DTC), K-Nearest Neighbors (KNN), and Bernoulli Naive Bayes (BNB). The classifiers are trained on the pre-processed training data, and their performance is evaluated using accuracy metrics, classification reports, and confusion matrices. Results offer insightful information on the weaknesses and weaknesses of each classifier for detecting network anomaly. The findings demonstrate that the DTC classifier exhibits high accuracy and robustness in detecting network anomalies. The KNN classifier also achieves competitive results but may suffer from scalability issues with large datasets. The SVM classifier demonstrates satisfactory performance, while the BNB classifier, which converts numeric features to binary form, exhibits relatively lower accuracy.