• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Book chapters
    • View Item
    • Home
    • Griffith Research Online
    • Book chapters
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Artificial Intelligence-Based Intrusion Detection Techniques

    Author(s)
    Jadidi, Zahra
    Muthukkumarasamy, Vallipuram
    Sithirasenan, Elankayer
    Griffith University Author(s)
    Muthukkumarasamy, Vallipuram
    Sithirasenan, Elankayer
    Jadidi, Zahra
    Year published
    2014
    Metadata
    Show full item record
    Abstract
    Our increasing dependence on different types of networks leads us to make them more secure. Intrusion detection is very challenging in all of those networks. An Intrusion detection system (IDS) attempts to discover malicious activities in a network. More sophisticated and increasing number of attacks are targeted against computer networks. Several methods have been proposed to provide accurate intrusion detection. Use of Artificial Intelligence (AI) in intrusion detection systems is wellknown. AI-based IDSs may detect even unknown attacks. On the other hand, network throughput is increasing and IDSs should be able to handle ...
    View more >
    Our increasing dependence on different types of networks leads us to make them more secure. Intrusion detection is very challenging in all of those networks. An Intrusion detection system (IDS) attempts to discover malicious activities in a network. More sophisticated and increasing number of attacks are targeted against computer networks. Several methods have been proposed to provide accurate intrusion detection. Use of Artificial Intelligence (AI) in intrusion detection systems is wellknown. AI-based IDSs may detect even unknown attacks. On the other hand, network throughput is increasing and IDSs should be able to handle the high volume of traffic in real-time. Different models have been proposed to improve the processing speed of these systems. Most studies consider IDSs in IP version 4 (IPv4). However, the migration to IP version 6 (IPv6) has already started and is inevitable. There are several security challenges in this migration process and hence, IDS becomes an essential tool for these networks. Evolution from conventional wired networks to other types of networks introduce another set of security threats. For example, cloud environment, grid computing and wireless networks open up several vulnerabilities which are easily exploited by attackers. Thus, the ability to protect such networks by IDS become increasingly challenging. This chapter discusses the applications of AI-based IDS in different environments.
    View less >
    Book Title
    The State of the Art in Intrusion Prevention and Detection
    Publisher URI
    http://www.crcpress.com/product/isbn/9781482203516
    Copyright Statement
    Self-archiving is not yet supported by this publisher. Please refer to the publisher website or contact the author(s) for more information.
    Subject
    Information and Computing Sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/61970
    Collection
    • Book chapters

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander