Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Anomaly detection in user behavior of websites using Hierarchical Temporal Memories: Using Machine Learning to detect unusual behavior from users of a web service to quickly detect possible security hazards.
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

This Master's Thesis focuses on the recent Cortical Learn-ing Algorithm (CLA), designed for temporal anomaly detection. It is here applied to the problem of anomaly detec-tion in user behavior of web services, which is getting moreand more important in a network security context.

CLA is here compared to more traditional state-of-the-art algorithms of anomaly detection: Hidden Markov Models (HMMs) and t-stide (an N-gram-based anomaly detector), which are among the few algorithms compatible withthe online processing constraint of this problem.

It is observed that on the synthetic dataset used forthis comparison, CLA performs signicantly better thanthe other two algorithms in terms of precision of the detection. The two other algorithms don't seem to be able tohandle this task at all. It appears that this anomaly de-tection problem (outlier detection in short sequences overa large alphabet) is considerably different from what hasbeen extensively studied up to now.

Place, publisher, year, edition, pages
2017. , 32 p.
Keyword [en]
anomaly detection, machine learning, HTM
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-206979OAI: oai:DiVA.org:kth-206979DiVA: diva2:1094877
External cooperation
Silca, Groupe Crédit Agricole (FRANCE)
Presentation
2017-04-04, room 4423, Lindstedtsvägen 5, Stockholm, 10:15 (English)
Supervisors
Examiners
Available from: 2017-05-19 Created: 2017-05-11 Last updated: 2017-05-19Bibliographically approved

Open Access in DiVA

fulltext(3961 kB)65 downloads
File information
File name FULLTEXT01.pdfFile size 3961 kBChecksum SHA-512
f1426c0a0b963ede752c165eca00108586f3cac6426d3e74fec7150404924a01e898a1085beeffa1c7d0f4eebb1522adedc97e324cd6f6c499ca5e62a70dda79
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 65 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 513 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf