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
310
Elastic Stack X-Pack 5.0 for IT Ops Workshop
kosho
0
330
[Developers Summit 2017] Anomaly Detection with the Elastic Stack
kosho
1
710
Anomaly Detection with the Elastic Stack
kosho
1
1.8k
Getting Started with Elastic Cloud and Beats for Log Analytics
kosho
0
97
Elastic{ON} Seminar Tokyo 2016 Product Update
kosho
0
170
Introducing Elastic Cloud
kosho
0
76
Gearing Up for Elastic Stack, X-Pack 5.0 Releases
kosho
0
150
Elastic Stack Hands-on Workshop (EN)
kosho
1
160
Other Decks in Technology
See All in Technology
Claude CodeでKiroの仕様駆動開発を実現させるには...
gotalab555
3
910
LIFF CLIとngrokを使ったLIFF/LINEミニアプリのお手軽実機確認
diggymo
0
230
ホリスティックテスティングの右側も大切にする 〜2つの[はか]る〜 / Holistic Testing: Right Side Matters
nihonbuson
PRO
0
590
AI人生苦節10年で会得したAIがやること_人間がやること.pdf
shibuiwilliam
1
270
AIのグローバルトレンド 2025 / ai global trend 2025
kyonmm
PRO
1
120
マルチモーダル基盤モデルに基づく動画と音の解析技術
lycorptech_jp
PRO
4
530
みんなのSRE 〜チーム全員でのSRE活動にするための4つの取り組み〜
kakehashi
PRO
2
140
2025-07-31: GitHub Copilot Agent mode at Vibe Coding Cafe (15min)
chomado
2
380
生成AI時代におけるAI・機械学習技術を用いたプロダクト開発の深化と進化 #BetAIDay
layerx
PRO
1
1k
ロールが細分化された組織でSREと協働するインフラエンジニアは何をするか? / SRE Lounge #18
kossykinto
0
190
AI関数が早くなったので試してみよう
kumakura
0
150
Claude Codeから我々が学ぶべきこと
s4yuba
9
2.2k
Featured
See All Featured
Product Roadmaps are Hard
iamctodd
PRO
54
11k
Mobile First: as difficult as doing things right
swwweet
223
9.9k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
jQuery: Nuts, Bolts and Bling
dougneiner
63
7.8k
Measuring & Analyzing Core Web Vitals
bluesmoon
7
540
Visualization
eitanlees
146
16k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
26k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
Being A Developer After 40
akosma
90
590k
Typedesign – Prime Four
hannesfritz
42
2.7k
Designing for Performance
lara
610
69k
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