Detecting and Coloring Anomalies in Real Cellular Network Using Principle Component Analysis

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

Abstract

Anomaly detection in a communication network is a powerful tool for predicting faults, detecting network sabotage attempts and learning user profiles for marketing purposes and quality of services improvements. In this article, we convert the unsupervised data mining learning problem into a supervised classification problem. We will propose three methods for creating an associative anomaly within a given commercial traffic data database and demonstrate how, using the Principle Component Analysis (PCA) algorithm, we can detect the network anomaly behavior and classify between a regular data stream and a data stream that deviates from a routine, at the IP network layer level. Although the PCA method was used in the past for the task of anomaly detection, there are very few examples where such tasks were performed on real traffic data that was collected and shared by a commercial company. The article presents three interesting innovations: The first one is the use of an up-to-date database produced by the users of an international communications company. The dataset for the data mining algorithm retrieved from a data center which monitors and collects low-level network transportation log streams from all over the world. The second innovation is the ability to enable the labeling of several types of anomalies, from untagged datasets, by organizing and prearranging the database. The third innovation is the abilities, not only to detect the anomaly but also, to coloring the anomaly type. I.e., identification, classification and labeling some forms of the abnormality.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings
EditorsItai Dinur, Shlomi Dolev, Sachin Lodha
PublisherSpringer Verlag
Pages68-83
Number of pages16
ISBN (Print)9783319941462
DOIs
StatePublished - 1 Jan 2018
Event2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018 - Beer-Sheva, Israel
Duration: 21 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10879 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018
Country/TerritoryIsrael
CityBeer-Sheva
Period21/06/1822/06/18

Keywords

  • Anomaly detection
  • Data mining
  • Machine learning
  • PCA

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