Reducing false rate packet recognition using Dual Counting Bloom Filter

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to Internet security. A lot of research effort focuses on having detection and prevention methods on the victim server side or source side. The Bloom filter is a space-efficient data structure used to support pattern matching problems. The filter is utilised in network applications for deep packet inspection of headers and contents and also looks for predefined strings to detect irregularities. In intrusion detection systems, the accuracy of pattern matching algorithms is crucial for dependable detection of matching pairs, and its complexity usually poses a critical performance bottleneck. In this paper, we will propose a novel Dual Counting Bloom Filter (DCBF) data structure to decrease false detection of matching packets applicable for the $$\textit{SACK}^2$$ algorithm. A theoretical evaluation will determine the false rate probability of detection and requirements for increased memory. The proposed approach significantly reduces the false rate compared to previously published results. The results indicate that the increased complexity of the DCBF does not affect efficient implementation of hardware for embedded systems that are resource constrained. The experimental evaluation was performed using extensive simulations based on real Internet traces of a wide area network link, and it was subsequently proved that DCBF significantly reduces the false rate.