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A Real-Time Intrusion Detection System Based on Learning Program Behavior

https://doi.org/10.1007/3-540-39945-3_7
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Efficient Learning of Typical Finite Automata from Random Walks
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The Nature of Statistical Learning Theory
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Detection of Abrupt Changes: Theory and Application
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Information bounds and quick detection of parameter changes in stochastic systems
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Intrusion detection via system call traces
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Detecting anomalous and unknown intrusions against programs
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A sense of self for Unix processes
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Detecting intrusions using system calls: alternative data models
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Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
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Computer immunology
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Crytographic limitations on learning Boolean formulae and finite automata
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Finding Structure in Time
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An immunological approach to change detection: algorithms, analysis and implications
inferredcites →https://doi.org/10.7916/d8b56rf2openalex-snapshot
Learning Patterns from Unix Process Execution Traces for Intrusion Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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STUDY OF ARTIFICIAL INTELLIGENCE IN CYBER SECURITY AND THE EMERGING THREAT OF AI-DRIVEN CYBER ATTACKS AND CHALLENGE
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Evolutionary Learning Program’s Behavior in Neural Networks for Anomaly Detection
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Integer Data Zero-Watermark Assisted System Calls Abstraction and Normalization for Host Based Anomaly Detection Systems
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On the Comparison of User Space and Kernel Space Traces in Identification of Software Anomalies
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Wavelet neural network optimization applied to intrusion detection
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Kolmogorov complexity based automata modeling for intrusion detection
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Simple, state-based approaches to program-based anomaly detection
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