eBF: an enhanced Bloom Filter for intrusion detection in IoT

Abstract Intrusion Detection is essential to identify malicious incidents and continuously alert many users of the Internet Things (IoT). The constant monitoring events generated from devices connected IoT extensive analysis every event based on predefined security policies consumes enormous resources. Accordingly, performance enhancement a crucial concern in other massive Big Data Applications ensure secure environment. Like Applications, system needs employ fast membership filter, Bloom Filter, quickly possible attacks. Filter an admiringly space-efficient data structure that handles elements datasets small memory space. However, trade-off between query performance, number hash functions, space, false positive probability remains issue Filter. Thus, this article presents enhanced (eBF) remarkably improves efficiency introduces new techniques accelerate filtering URLs. We experimentally show efficacy eBF using real dataset. experimental result shows proposed filter efficient, faster, more accurate than state-of-the-art filters. requires 15.6x, 13x, 8x less compared with Standard Cuckoo robustBF, respectively. Therefore, significantly enhances concurrently monitors several billion crosschecking defined policies.