Using Bandits for Effective Database Activity Monitoring

Hagit Grushka-Cohen, Ofer Biller, Oded Sofer, Lior Rokach, Bracha Shapira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Database activity monitoring systems aim to protect organizational data by logging users’ activity to Identify and document malicious activity. High-velocity streams and operating costs, restrict these systems to examining only a sample of the activity. Current solutions use manual policies to decide which transactions to monitor. This limits the diversity of the data collected, creating a “filter bubble” over representing specific subsets of the data such as high-risk users and under-representing the rest of the population which may never be sampled. In recommendation systems, Bandit algorithms have recently been used to address this problem. We propose addressing the sampling for database activity monitoring problem as a recommender system. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit problem and present a novel algorithm, (Formula Presented)–Greedy, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection using simulated data. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task, maximizing population coverage without decreasing the quality in terms of issuing alerts about events, and outperforming policies manually crafted by experts and other sampling methods.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
EditorsHady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
PublisherSpringer
Pages701-713
Number of pages13
ISBN (Print)9783030474355
DOIs
StatePublished - 1 Jan 2020
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12085 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Country/TerritorySingapore
CitySingapore
Period11/05/2014/05/20

Keywords

  • Database activity monitoring
  • Filter bubble
  • Multi-armed bandit
  • Sampling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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