A Novel Approach based on Improved Naive Bayes for 5G Air Interface DDoS Detection

The network security architecture of 5G defined by 3rd Generation Partnership Project (3GPP) in version 15, is vulnerable to the attack on wireless transmission due to its imperfect security design. The Distributed Denial of Service (DDoS) Attack is currently one of the biggest threats to air interface security of 5G. Attackers use controlled equipment to send a large number of authentication signaling to target base station, forming an instantaneous DDoS attack to achieve the purpose of destroying available resources. DDoS attack is simple to operate, hard to detect, and hugely harmful. From the perspective of signaling changes, this paper focuses on analyzing the authentication process that is most prone to DDoS attack, and extracts seven features based on the signaling change of the authentication process. We innovatively establish a classifier to detect DDoS attack using a novel improved naive Bayes algorithm. The algorithm not only comprehensively considers the independency and dependency between features through structural improvement, but also uses genetic algorithm to solve the optimal combination of attributes for category weights. At the end of the paper, we give simulation results which show the effectiveness of the proposed algorithm in detecting DDoS attack.