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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document : 462 / 462

한글제목(Korean Title) Feature Selection Algorithm for Intrusions Detection System using Sequential Forward Search and Random Forest Classifier
영문제목(English Title) Feature Selection Algorithm for Intrusions Detection System using Sequential Forward Search and Random Forest Classifier
저자(Author) Jinlee Lee   Dooho Park   Changhoon Lee                       
원문수록처(Citation) VOL 11 NO. 10 PP. 5132 ~ 5148 (2017. 10)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.
키워드(Keyword) FeatureSelection   SFFS   RandomForest   IDS                    
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