JOURNAL ARTICLE

Operator-Centric and Adaptive Intrusion Detection

Abstract

An intrusion detection system should support the operator of the system. Thus, in addition to producing alerts, it should allow for easy insertion of new detection algorithms. It should also support dynamic selection and de-selection of detection algorithms, and it should adjust its resource consumption to the current need. Such a system would allow the operator to easily extend the system when new detection algorithms become available. It would also allow the operator to maintain a low-cost monitoring baseline and perform more extensive monitoring when it is required. In this paper we propose an architecture for intrusion detection which aims at providing the operator with this support. The architecture uses a modular design to promote a high degree of flexibility. This supports creation of an environment in which state-of-the-art intrusion detection algorithms easily can be inserted. The modular design also allows for detection algorithms to be enabled and disabled when required. Additionally, the architecture uses a sensor reconfiguration mechanism to affect the amount of data collected. When a detection algorithm is enabled or disabled, the sensor providing the input data to the algorithm is correspondingly reconfigured. This implies a minimum of excess collected data. To illustrate the feasibility of the architecture, we provide a proof-of-concept supporting monitoring of users for insider detection and webserver monitoring for intrusion attempts.

Keywords:
Intrusion detection system Computer science Modular design Flexibility (engineering) Operator (biology) Control reconfiguration Architecture Distributed computing Real-time computing Embedded system Data mining Operating system

Metrics

7
Cited By
1.59
FWCI (Field Weighted Citation Impact)
19
Refs
0.88
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Network Security and Intrusion Detection
Physical Sciences →  Computer Science →  Computer Networks and Communications
Advanced Malware Detection Techniques
Physical Sciences →  Computer Science →  Signal Processing
Anomaly Detection Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence

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