TY - GEN
T1 - Improving the performance of neural networks with random forest in detecting network intrusions
AU - Li, Wenjuan
AU - Meng, Yuxin
PY - 2013
Y1 - 2013
N2 - Neural Networks such as RBFN and BPNN have been widely studied in the area of network intrusion detection, with the purpose of detecting a variety of network anomalies (e.g., worms, malware). In real-world applications, however, the performance of these neural networks is dynamic regarding the use of different datasets. One of the reasons is that there are some redundant features for the dataset. To mitigate this issue, in this paper, we propose an approach of combining Neural Networks with Random Forest to improve the accuracy of detecting network intrusions. In particular, we design an intelligent anomaly detection system that uses the algorithm of Random Forest in the process of feature selection and selects an appropriate algorithm in an adaptive way. In the evaluation, we conducted two major experiments using the KDD1999 dataset and a real dataset respectively. The experimental results indicate that Random Forest can enhance the performance of Neural Networks by identifying important and closely related features and that our developed system can select a better algorithm intelligently.
AB - Neural Networks such as RBFN and BPNN have been widely studied in the area of network intrusion detection, with the purpose of detecting a variety of network anomalies (e.g., worms, malware). In real-world applications, however, the performance of these neural networks is dynamic regarding the use of different datasets. One of the reasons is that there are some redundant features for the dataset. To mitigate this issue, in this paper, we propose an approach of combining Neural Networks with Random Forest to improve the accuracy of detecting network intrusions. In particular, we design an intelligent anomaly detection system that uses the algorithm of Random Forest in the process of feature selection and selects an appropriate algorithm in an adaptive way. In the evaluation, we conducted two major experiments using the KDD1999 dataset and a real dataset respectively. The experimental results indicate that Random Forest can enhance the performance of Neural Networks by identifying important and closely related features and that our developed system can select a better algorithm intelligently.
KW - Anomaly Intrusion Detection
KW - Intelligent Applications
KW - Neural Networks
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=84880758339&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39068-5_74
DO - 10.1007/978-3-642-39068-5_74
M3 - Conference article published in proceeding or book
AN - SCOPUS:84880758339
SN - 9783642390678
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 622
EP - 629
BT - Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
PB - Springer Verlag
T2 - 10th International Symposium on Neural Networks, ISNN 2013
Y2 - 4 July 2013 through 6 July 2013
ER -