The Effectiveness of a Random Forests Model in Detecting Network-Based Buffer Overflow Attacks.

详细信息   

• 作者：Julock ; Gregory Alan.
• 学历：Ph.D.
• 年：2013
• 导师：Li, Wei,eadvisorCannady, James D.ecommittee memberMukherjee, Sumitraecommittee member
• 毕业院校：Nova Southeastern University
• Department：Computer Information Systems (MCIS, DCIS)
• ISBN：9781303348853
• CBH：3593016
• Country：USA
• 语种：English
• FileSize：8136690
• Pages：136

文摘

Buffer Overflows are a common type of network intrusion attack that continue to plague the networked community. Unfortunately, this type of attack is not well detected with current data mining algorithms. This research investigated the use of Random Forests, an ensemble technique that creates multiple decision trees, and then votes for the best tree. The research Investigated Random Forests' effectiveness in detecting buffer overflows compared to other data mining methods such as CART and Naï；ve Bayes. Random Forests was used for variable reduction, cost sensitive classification was applied, and each method's detection performance compared and reported along with the receive operator characteristics. The experiment was able to show that Random Forests outperformed CART and Naï；ve Bayes in classification performance. Using a technique to obtain Buffer Overflow most important variables, Random Forests was also able to improve upon its Buffer Overflow classification performance.