Towards designing packet filter with a trust-based approach using Bayesian inference in network intrusion detection

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.