DocumentCode :
3301326
Title :
LLE on System Calls for Host Based Intrusion Detection
Author :
Dash, Subrat Kumar ; Rawat, Sanjay ; Pujari, Arun K.
Author_Institution :
Artificial Intelligence Lab., Hyderabad Univ.
Volume :
1
fYear :
2006
fDate :
Nov. 2006
Firstpage :
609
Lastpage :
612
Abstract :
In this paper we examine the manifold learning approach for anomaly detection of sequences of system calls. We note that dimensionality reduction is very crucial for intrusion detection particularly when the training data is segmented into high-dimensional subsequences. We demonstrate that by applying manifold learning technique we can achieve substantial improvement in detection accuracy reducing the false positives. We examine the applicability of manifold learning in two different approaches. In the first approach, we represent the system call data as vectors by capturing the term frequencies and in the second approach; we represent the data as a decision table. We demonstrate that in both modes of representation, manifold learning method gives better result for the benchmark data sets
Keywords :
decision tables; learning (artificial intelligence); security of data; LLE; anomaly detection; benchmark data sets; decision table; dimensionality reduction; host-based intrusion detection; manifold learning; system call sequences; system calls; Artificial intelligence; Communication networks; Computer networks; Data analysis; Frequency; Intrusion detection; Learning systems; Manifolds; Training data; Weight measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security, 2006 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
1-4244-0605-6
Electronic_ISBN :
1-4244-0605-6
Type :
conf
DOI :
10.1109/ICCIAS.2006.294207
Filename :
4072160
Link To Document :
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