Security Enhancement in Distributed Networks Using Link-Based Mapping Scheme for Network Intrusion Detection with Enhanced Bloom Filter

To prevent and monitor the unauthorized usage of data access, security on the network is implemented by authenticating the data. Network intrusion detection system monitors the network traffic and measures the information to identify the suspicious activities. In distributed networks, the network administrator has to authorize the user data access. When large data set is concerned in network applications the two complex issues to be solved are the organization of information and decision making. To address these issues, a space efficient data structure, called the bloom filter is used which effectively organizes and decides the presence of reliability. However, using advanced filtering techniques, the intruders easily hack the authorized data for unauthorized operations. At the same time, when processing the information, it is difficult to access the data in a secured manner using the standard bloom filters. To enhance the security over the user data access from the intruders, an enhanced bloom filter technique is presented to represent the large set of data in secure manner applied in distributed applications like web caching, peer networks etc. Additionally, to restrict the unauthorized access over the dataset from malicious activities by intruders, the enhanced bloom filter is applied with an upper bound on the false-positive probability by increasing its capacity as the packet data size increases. The occurrence of network data traffic is cleared by mapping the set of data elements to the appropriate setting in the database using hash functions, minimizing the number of resets created and at the same time improving the mean hit ratio. An experimental evaluation is done with the KDD cup 1999 dataset extracted from UCI repository to estimate the performance of the proposed link-based mapping for network intrusion detection system with enhanced bloom filters. Performance evaluation is measured in terms of false positive probability, false negative probability, mean hit ratio, scalability, number of resets created and security. The experimental results reveals that security over the packet data achieves 42.5 % higher against existing dynamic bloom filter approach.

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