Enhancing Network Security Algorithm Using Machine Learning

In light of the increasing sophistication of cyber threats in today's linked world, network security is of paramount importance. Monitoring network traffic and detecting suspicious activity is where intrusion detection systems (IDS) shine, playing a crucial role in identifying and managing these threats. There has been a rise in the use of machine learning methods to improve IDS performance. In order to improve network safety, a new hybrid method is proposed in this study, which combines Random Forest (RF) with Support Vector Machine (SVM) to perform intrusion detection. The proposed hybrid RF and SVM approach draws on the best features of both models to enhance intrusion detection precision and reliability. As an ensemble learning technique, Random Forest excels at both feature selection and handling big datasets. Conversely, Support Vector Machine has earned acclaim for its prowess in binary classification problems and its ability to locate complicated decision boundaries. We want to improve upon intrusion detection by integrating these two techniques, which have complimentary characteristics. The first step in this study is to compile a large dataset of legitimate and malicious network traffic. A preprocessing pipeline is built up to guarantee the quality of the data, and feature engineering techniques are used to extract useful information from the dataset. This preprocessed dataset is then used to train a hybrid RF and SVM algorithm using labelled data containing known intrusion cases. Extensive tests and comparisons to other intrusion detection methods are used to assess the performance of the proposed algorithm. The efficiency of the hybrid RF and SVM algorithm in identifying network intrusions is evaluated using performance measures like as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC-AUC) curve. This research shows that a system that combines RF and SVM for intrusion detection may beat both conventional methods and stand-alone models in terms of accuracy, detection rate, and false-positive rate. The interpretability qualities of the proposed algorithm also make it a significant tool for security analysts to comprehend and efficiently respond to network threats. This study introduces an innovative method for improving network safety by incorporating machine learning techniques, especially a hybrid RF and SVM algorithm, into IDS. In the face of constantly developing cyber threats, the combination of these two models provides a more resilient and effective network security solution with increased detection accuracy and interpretability.