Application of frequent item set mining algorithm in IDS based on Hadoop framework
Tong Zhang, Hou Ying · 2018
With the coming of big data time, the huge number of IDS log makes the traditional computing technology and systems cannot cope and deal with the needs of the analysis of security log, so large-scale computing power has become a prerequisite for the effective implementation of data mining technology. Based on the Hadoop framework, this paper applies the parallel frequent item sets mining algorithm to the Snort Intrusion Detection System, which solves the problem that Snort-IDS cannot judge the security event itself. At the same time, it also solves the problem of decreasing the processing speed due to the enormous increase of data. So that the system has the ability of detecting new intrusions, enrich and improve the Snort-IDS functional system and the performance.