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Network intrusion detection

Published: 01 May 1994 Publication History

Abstract

Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging task due to the proliferation of heterogeneous computer networks since the increased connectivity of computer systems gives greater access to outsiders and makes it easier for intruders to avoid identification. Intrusion detection systems (IDSs) are based on the beliefs that an intruder's behavior will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable. Typically, IDSs employ statistical anomaly and rulebased misuse models in order to detect intrusions. A number of prototype IDSs have been developed at several institutions, and some of them have also been deployed on an experimental basis in operational systems. In the present paper, several host-based and network-based IDSs are surveyed, and the characteristics of the corresponding systems are identified. The host-based systems employ the host operating system's audit trails as the main source of input to detect intrusive activity, while most of the network-based IDSs build their detection mechanism on monitored network traffic, and some employ host audit trails as well. An outline of a statistical anomaly detection algorithm employed in a typical IDS is also included

Cited By

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  • (2024)SAROS: A Self-Adaptive Routing Oblivious Sampling Method for Network-wide Heavy Hitter DetectionProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3663429(142-148)Online publication date: 3-Aug-2024
  • (2024)Attenuating majority attack class bias using hybrid deep learning based IDS frameworkJournal of Network and Computer Applications10.1016/j.jnca.2024.103954230:COnline publication date: 18-Oct-2024
  • (2024)ApollonComputers and Security10.1016/j.cose.2023.103546136:COnline publication date: 1-Feb-2024
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cover image IEEE Network: The Magazine of Global Internetworking
IEEE Network: The Magazine of Global Internetworking  Volume 8, Issue 3
May 1994
42 pages

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IEEE Press

Publication History

Published: 01 May 1994

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Cited By

View all
  • (2024)SAROS: A Self-Adaptive Routing Oblivious Sampling Method for Network-wide Heavy Hitter DetectionProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3663429(142-148)Online publication date: 3-Aug-2024
  • (2024)Attenuating majority attack class bias using hybrid deep learning based IDS frameworkJournal of Network and Computer Applications10.1016/j.jnca.2024.103954230:COnline publication date: 18-Oct-2024
  • (2024)ApollonComputers and Security10.1016/j.cose.2023.103546136:COnline publication date: 1-Feb-2024
  • (2024)A computationally efficient dimensionality reduction and attack classification approach for network intrusion detectionInternational Journal of Information Security10.1007/s10207-023-00792-x23:3(2457-2487)Online publication date: 1-Jun-2024
  • (2023)Poisoning Network Flow ClassifiersProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627123(337-351)Online publication date: 4-Dec-2023
  • (2023)Multi-Objective Optimization on Autoencoder for Feature Encoding and Attack Detection on Network DataProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590600(379-382)Online publication date: 15-Jul-2023
  • (2023)Implementing Data Exfiltration Defense in Situ: A Survey of Countermeasures and Human InvolvementACM Computing Surveys10.1145/358207755:14s(1-37)Online publication date: 25-Jan-2023
  • (2023)Zero Trust Network Intrusion Detection System (NIDS) using Auto Encoder for Attention-based CNN-BiLSTMProceedings of the 2023 Australasian Computer Science Week10.1145/3579375.3579376(1-9)Online publication date: 30-Jan-2023
  • (2023)CSCAD: Correlation Structure-Based Collective Anomaly Detection in Complex SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315416635:5(4634-4645)Online publication date: 1-May-2023
  • (2023)Feature Selection Using a Combination of Ant Colony Optimization and Random Forest Algorithms Applied To Isolation Forest Based Intrusion Detection SystemProcedia Computer Science10.1016/j.procs.2023.03.106220:C(796-805)Online publication date: 10-May-2023
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