Detecting APT Attacks Based on Network Traffic Using Machine Learning

Advanced Persistent Threat (APT) attacks are a form of malicious, intentionally and clearly targeted attack. By using many sophisticated and complicated methods and technologies to attack targets in order to obtain confidential and sensitive information. In fact, in order to detect APT attacks, detection systems often need to apply many parallel and series techniques in order to make the most of the advantages as well as minimize the disadvantages of each technique. Therefore, in this paper, we propose a method of detecting APT attacks based on abnormal behaviors of Network traffic using machine learning. Accordingly, in our research, the abnormal behavior of APT attacks in Network Traffic will be defined on both components: Domain and IP. Then, these behaviors are evaluated and classified based on the Random Forest classification algorithm to conclude about the behavior of APT attacks. Details of the definition of abnormal behaviors of the Domain and IP will be presented in Section 3.2 of the paper. The synchronous APT attack detection method proposed in this paper is a novel approach, which will help information security systems detect quickly and accurately signs of the APT attack campaign in the organization. The experimental results presented in Section 4 will demonstrate the effectiveness of our proposed method.