Research Article
Towards Designing Packet Filter with a Trust-Based Approach Using Bayesian Inference in Network Intrusion Detection
@INPROCEEDINGS{10.1007/978-3-642-36883-7_13, author={Yuxin Meng and Lam-For Kwok and Wenjuan Li}, title={Towards Designing Packet Filter with a Trust-Based Approach Using Bayesian Inference in Network Intrusion Detection}, proceedings={Security and Privacy in Communication Networks. 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers}, proceedings_a={SECURECOMM}, year={2013}, month={2}, keywords={Packet Filter IP Confidence Trust Calculation Network Intrusion Detection Bayesian Inference}, doi={10.1007/978-3-642-36883-7_13} }- Yuxin Meng
Lam-For Kwok
Wenjuan Li
Year: 2013
Towards Designing Packet Filter with a Trust-Based Approach Using Bayesian Inference in Network Intrusion Detection
SECURECOMM
Springer
DOI: 10.1007/978-3-642-36883-7_13
Abstract
Network intrusion detection systems (NIDSs) have become an essential part for current network security infrastructure. However, in a large-scale network, the overhead network packets can greatly decrease the effectiveness of such detection systems by significantly increasing the processing burden of a NIDS. To mitigate this issue, we advocate that constructing a packet filter is a promising and complementary solution to reduce the workload of a NIDS, especially to reduce the burden of signature matching. We have developed a blacklist-based packet filter to help a NIDS filter out network packets and achieved positive experimental results. But the calculation of IP confidence is still a big challenge for our previous work. In this paper, we further design a packet filter with a trust-based method using Bayesian inference to calculate the IP confidence and explore its performance with a real dataset and in a network environment. We also analyze the trust-based method by comparing it with our previous weight-based method. The experimental results show that by using the trust-based calculation of IP confidence, our designed trust-based blacklist packet filter can achieve a better outcome.
