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Service specific anomaly detection for network intrusion detection
Abstract
The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.
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Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663
Casting out Demons: Sanitizing Training Data for Anomaly Sensors
Cretu, G. F., Stavrou, A., Locasto, M. E., Stolfo, S. J., & Keromytis, A. D. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.11 · Cited by 226
Detection of Anomalies in Large-Scale Cyberattacks Using Fuzzy Neural Networks
de Campos Souza, P. V., Guimarães, A. J., Rezende, T. S., Silva Araujo, V. J., & Araujo, V. S. (2020) · AI · doi:10.3390/ai1010005 · Cited by 16
Metadata
- Authors
- Krügel, C., Toth, T., & Kirda, E.
- Venue
- Proceedings of the 2002 ACM symposium on Applied computing
- Publisher
- ACM
- Published
- 2002-03-11
- Pages
- 201-208
- OpenAlex
- W1993426957
- Cited by
- 306
- References
- 0
- FWCI
- 5.93
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Krügel, C., Toth, T., & Kirda, E. (2002). Service specific anomaly detection for network intrusion detection. Proceedings of the 2002 ACM symposium on Applied computing. https://doi.org/10.1145/508791.508835
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