Machine-Learning Based TCP Security Action Prediction

With the continuous growth of Internet technology and the increasingly broadening applications of The Internet, network security incidents as well as cyber-attacks are also showing a growing trend. Consequently, computer network security is becoming increasingly important. TCP firewall is a computer network security system, and it allows or denies the transmission of data according to specific rules for providing security for the computer network. Traditional firewalls rely on network administrators to set security rules for them, and network administrators sometimes need to choose to allow and deny packets to keep computer networks secure. However, due to the huge amount of data on the Internet, network administrators have a huge task. Therefore, it is particularly important to solve this problem by using the machine learning method of computer technology. This study aims to predict TCP security action based on the TCP transmission characteristics dataset provided by UCI machine learning repository by implementing machine learning models such as neural network, support vector machine (SVM), AdaBoost, and Logistic regression. Processes including evaluating various models and interpretability analysis. By utilizing the idea of ensemble-learning, the final result has an accuracy score of over 98%.