Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Introducing Machine Learning for the Elastic Stack
Search
Kosho Owa
May 19, 2017
Technology
2
12k
Introducing Machine Learning for the Elastic Stack
Elastic Machine Learning Seminar held on May 19th, 2017
Kosho Owa
May 19, 2017
Tweet
Share
More Decks by Kosho Owa
See All by Kosho Owa
Elastic Stack X-Pack 5.0 for IT Security Workshop
kosho
1
290
Elastic Stack X-Pack 5.0 for IT Ops Workshop
kosho
0
310
[Developers Summit 2017] Anomaly Detection with the Elastic Stack
kosho
1
680
Anomaly Detection with the Elastic Stack
kosho
1
1.8k
Getting Started with Elastic Cloud and Beats for Log Analytics
kosho
0
89
Elastic{ON} Seminar Tokyo 2016 Product Update
kosho
0
160
Introducing Elastic Cloud
kosho
0
64
Gearing Up for Elastic Stack, X-Pack 5.0 Releases
kosho
0
130
Elastic Stack Hands-on Workshop (EN)
kosho
1
150
Other Decks in Technology
See All in Technology
東京Ruby会議12 Ruby と Rust と私 / Tokyo RubyKaigi 12 Ruby, Rust and me
eagletmt
3
870
Goで実践するBFP
hiroyaterui
1
120
AWS re:Invent 2024 recap in 20min / JAWSUG 千葉 2025.1.14
shimy
1
100
Azureの開発で辛いところ
re3turn
0
240
データ基盤におけるIaCの重要性とその運用
mtpooh
4
530
AWS re:Invent 2024 re:Cap Taipei (for Developer): New Launches that facilitate Developer Workflow and Continuous Innovation
dwchiang
0
170
メンバーがオーナーシップを発揮しやすいチームづくり
ham0215
2
140
GoogleのAIエージェント論 Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic
customercloud
PRO
0
160
深層学習と3Dキャプチャ・3Dモデル生成(土木学会応用力学委員会 応用数理・AIセミナー)
pfn
PRO
0
460
.NET 最新アップデート ~ AI とクラウド時代のアプリモダナイゼーション
chack411
0
200
re:Invent 2024のふりかえり
beli68
0
110
機械学習を「社会実装」するということ 2025年版 / Social Implementation of Machine Learning 2025 Version
moepy_stats
5
1.2k
Featured
See All Featured
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
45
2.3k
How GitHub (no longer) Works
holman
312
140k
A designer walks into a library…
pauljervisheath
205
24k
Building Flexible Design Systems
yeseniaperezcruz
328
38k
Designing for Performance
lara
604
68k
Why You Should Never Use an ORM
jnunemaker
PRO
54
9.1k
Optimising Largest Contentful Paint
csswizardry
33
3k
Designing for humans not robots
tammielis
250
25k
Facilitating Awesome Meetings
lara
51
6.2k
Docker and Python
trallard
43
3.2k
A Tale of Four Properties
chriscoyier
157
23k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
7k
Transcript
Machine Learning for the Elastic Stack Beta in 5.4.
GA coming soon May 2017 େྠ ߂ৄ | Kosho Owa Solutions Architect, Elastic
2 Elastic Stack 100% Φʔϓϯιʔε ʮΤϯλʔϓϥΠζ൛ʯແ͠ όʔδϣϯ 5.0Ͱશ౷Ұ
3 X-Pack ؆୯ʹΠϯετʔϧ Elastic StackΛ֦ு αϒεΫϦϓγϣϯʹؚ·ΕΔ Security Alerting Monitoring Reporting
Graph Machine Learning
4 Elastic Cloud Elasticsearch, Kibanaͷ ϚωʔδυαʔϏε X-Packͷػೳར༻Մೳ Available in AWS
today
5 Elastic Cloud Enterprise ෳͷElastic StackڥΛࣗࡏʹ࡞ Logging as a serviceΛࣗ৫ʹల։
Public beta; Expected GA Q1 2017
ҟৗͷൃݟ͕τϥϒϧͷஹީΛࣔ͢ 6 Spiked 404 errors Web attack IT Operational Analytics
Security Analytics Business Analytics Unusual DNS activity Data exfiltration Rare log messages Failing sensor
Operational Analytics • ΣϒαΠτͷΞΫηετϥϑΟοΫʹҟৗແ͍͔? • Ϙοτ߈ܸऀ͕๚Ε͍ͯͳ͍͔? • σʔλϕʔε͕ग़ྗ͍ͯ͠ΔErrorϩάରॲ͢Δඞཁ͕ ͋Δͷ͔? Use
Case
Security Analytics • ϚϧΣΞʹ৵ೖ͞Ε͍ͯͳ͍͔? • ෦ऀʹΑΔηΩϡϦςΟڴҖແ͍͔? • DNSͷϩάʹɺσʔλऔͷ͕ࠟͳ͍͔? Use Case
Telemetry / Sensors ▪ ISPͷωοτϫʔΫҰ࣌ःஅʹΑΔϨΠςϯγʔͷٸ ܹͳ૿Ճ? ▪ ଞͱҟͳΔӡసύλʔϯΛͱΔυϥΠόʔ? ▪ ಛҟͳΠϕϯτλΠϓηϯαʔͷނোΛ͔ࣔ͢?
Use Case
10 ҟৗͷൃݟࢥͬͨΑΓ͍͠ • σʔλෳࡶɺߴ࣍ݩɺߴʹมԽ • ਓؒͷࢹೝݱ࣮తʹෆՄೳ • ༰қʹݟಀ͢ Visual inspection
is not practical Where’s the anomaly?
11 ҟৗͷൃݟࢥͬͨΑΓ͍͠ • ੩తͳᮢʹΑΔʮਖ਼ৗʯͷఆٛࠔ • ϧʔϧσʔλΠϯϑϥͷมߋʹैͰ͖ͳ͍ • ༰қʹᷖճ͞Εͯ͠·͏ Rule-based alerts
are insufficient What’s the right threshold ?
X-Pack͕ࣗಈతͳҟৗݕͰղܾ 12 • ʮڭࢣͳ͠ʯػցֶशςΫχοΫʹΑΓ ▪ աڈͷσʔλ͔Βʮਖ਼ৗʯΛֶͼϞσϧΛ࡞Δ ▪ ਖ਼ৗͷൣғ͔Βҳͨ͠ࡍʹҟৗͱͯ͠ݕ
X-Pack͕ࣗಈతͳҟৗݕͰղܾ 13 • ڭࢣͳ͠ - खಈͰͷਖ਼ৗͷೖྗ͕ෆཁ • σʔλͷมԽʹै - ೖ͞ΕΔσʔλʹΑΓܧଓతʹϞσϧΛߋ৽
• ӨڹҼࢠಛఆ - ࠜຊݪҼղੳΛՃ
ҟͳΔछྨͷҟৗΛݕ 14 • ࣌ܥྻͷϝτϦοΫ Time series - single / multiple
• ͙Εऀ Outliers in population (using entity profiling) • ك༗ͳඇߏϝοηʔδ Rare / unusual rates in “categories” of events
࣌ܥྻσʔλͷҟৗ 15 Time Metric • Single (univariate) time series Example:
Is there unusual traffic on website ?
࣌ܥྻσʔλͷҟৗ 16 Time Metric USA UK France Japan • Multiple
time series ▪ ෳͷϝτϦοΫ ▪ FieldʹΑͬͯྨ͞ΕͨϝτϦοΫ • ͦΕͧΕ͕ಠཱͯ͠ଘࡏ͢Δ Example: Is there unusual web activity from any country?
͙Εऀ Outliers in population (using entity profiling) 17 • ूஂͷಛ(server,
user, IPͳͲ)͔ΒϓϩϑΝΠϧΛ࡞͢Δ • ͜ͷूஂ͔Βҳ͢ΔͷΛൃݟ͢Δ Example: • Which IP address is not like the others? (indication of a bot / attacker)
͙Εऀ Outliers in population (using entity profiling) 18 • ूஂͷಛ(server,
user, IPͳͲ)͔ΒϓϩϑΝΠϧΛ࡞͢Δ • ͜ͷूஂ͔Βҳ͢ΔͷΛൃݟ͢Δ Example: • Which IP address is not like the others? (indication of a bot / attacker)
ك༗ͳඇߏϝοηʔδͷมԽ Unusual or rare events (via log categorization) 19 •
ྨࣅੑʹج͍ͮͯΧςΰϦ͚ • ࣌ؒมԽʹΑΔසΛֶश • ϞσϧͱҟͳΕҟৗͱͯ͠ݕ Example: • Do my application logs contain unusual messages
X-Pack Machine Learning Elastic StackͱͷڧݻͳΠϯςάϨʔγϣϯ 20
• Elasticsearch • Kibana ༰қʹΠϯετʔϧ 21 $ elasticsearch-plugin install x-pack
$ kibana-plugin install x-pack
σϓϩΠϝϯτϞσϧ 22 Cluster Data node Apps Master node Data node
Data node Master node Master node Data node Data node ES clients, Kibana, Logstash, Beats, User apps and etc. ML node ML node # config/elasticsearch.yml xpack.ml.enabled: true node.ml: true
֎෦γεςϜͱͷଓ • API (anomaly_detectors, datafeeds, results, model_snapshots, validate) • ΠϯσοΫε
(.ml-anomalies-*)
Taking Action with X-Pack Alerting 24
Demo Single/Multiple Metrics: New York City Yellow Taxi Outliers in
Population: Web Server Log Rare Messages: DBMS Server Log 25
26 4JOHMF.FUSJD
27 .VMUJ.FUSJD
28 .VMUJ.FUSJD
29 0VUMJFSTJO1PQVMBUJPO
30 0VUMJFSTJO1PQVMBUJPO
31 3BSF.FTTBHFT
32 3BSF.FTTBHFT
࣍ͷεςοϓ 33 • Elastic StackΛ·ͩར༻͍ͯ͠ͳ͍ • ϋϯζΦϯϫʔΫγϣοϓ • Elastic StackɺX-PackΛΠϯετʔϧ
• αϯϓϧσʔλΛར༻ (ϒϩάࢀর) or ࣗͷσʔλΛೖ • MLδϣϒΛ࡞ • Elastic StackΛར༻த • X-PackΛΠϯετʔϧ (30ؒͷτϥΠΞϧ/ඇϓϩμΫγϣϯڥ) • MLδϣϒΛ࡞ (Ϩγϐ׆༻) • AlertingͰΞΫγϣϯ