SVM-DT-Based Adaptive and Collaborative Intrusion Detection

• 作 者: Shaohua Teng Naiqi Wu Haibin Zhu Luyao Teng Wei Zhang
• 机 构: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, Chi
• 出 处: 自动化学报（英文版）
• 发表日期: 2018年1期
• 关键词: Adaptive and collaborative intrusion detection decision tree(DT) support vector

As a primary defense technique,intrusion detection becomes more and more significant since the security of the networks is one of the most critical issues in the world.We present an adaptive collaboration intrusion detection method to improve the safety of a network.A self-adaptive and collaborative intrusion detection model is built by applying the Environmentsclasses,agents,roles,groups,and objects (E-CARGO) model.The objects,roles,agents,and groups are designed by using decision trees (DTs) and support vector machines (SVMs),and adaptive scheduling mechanisms are set up.The KDD CUP 1999 data set is used to verify the effectiveness of the method.The experimental results demonstrate the feasibility and efficiency of the proposed collaborative and adaptive intrusion detection method.Also,the proposed method is shown to be more predominant than the methods that use a set of single type support vector machine (SVM) in terms of detection precision rate and recall rate.