MaNaDAC: An Effective Alert Correlation Method View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Manaswita Saikia , Nazrul Hoque , Dhruba Kumar Bhattacharyya

ABSTRACT

This paper presents an effective alert correlation method referred to as MaNaDAC to support network intrusion detection. The method includes several modules such as feature ranking and selection, clustering and fusion to process low-level alerts and uses the concept of causality to discover relations among attacks. The method has been validated using DARPA 2000 intrusion dataset. More... »

PAGES

249-260

References to SciGraph publications

  • 2017. Network Traffic Anomaly Detection and Prevention in NONE
  • 2003. Statistical Causality Analysis of INFOSEC Alert Data in RECENT ADVANCES IN INTRUSION DETECTION
  • 2000. LAMBDA: A Language to Model a Database for Detection of Attacks in RECENT ADVANCES IN INTRUSION DETECTION
  • 2001-09-27. Aggregation and Correlation of Intrusion-Detection Alerts in RECENT ADVANCES IN INTRUSION DETECTION
  • 2001-09-27. Probabilistic Alert Correlation in RECENT ADVANCES IN INTRUSION DETECTION
  • 2001. ADeLe: An Attack Description Language for Knowledge-Based Intrusion Detection in TRUSTED INFORMATION
  • 2002. M2D2: A Formal Data Model for IDS Alert Correlation in RECENT ADVANCES IN INTRUSION DETECTION
  • Book

    TITLE

    Recent Developments in Machine Learning and Data Analytics

    ISBN

    978-981-13-1279-3
    978-981-13-1280-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-981-13-1280-9_24

    DOI

    http://dx.doi.org/10.1007/978-981-13-1280-9_24

    DIMENSIONS

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