Massive Data Mining for Polymorphic Code Detection
作者： Udo Payer , Peter Teufl , Stefan Kraxberger , Mario Lamberger
DOI: 10.1007/11560326_38
关键词: Self-organizing map 、 Intrusion detection system 、 Genetic algorithm 、 Information extraction 、 Polymorphic code 、 Markov model 、 Data mining 、 Artificial neural network 、 Shellcode 、 Computer science
摘要: Driven by the permanent search for reliable anomaly-based intrusion detection mechanisms, we investigated different statistical methodologies to deal with of polymorphic shellcode. The paper intends give an overview on existing approaches in literature as well a synopsis our efforts evaluate applicability data mining techniques such Neural Networks, Self Organizing Maps, Markov Models or Genetic Algorithms area code detection. We will then present achieved results and conclusions.
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