Signature Based Intrusion Detection Using Latent Semantic Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Lassez:2008:ijcnn,

• author = "Jean-Louis Lassez and Ryan Rossi and Stephen Sheel and Srinivas Mukkamala",
• title = "Signature Based Intrusion Detection Using Latent Semantic Analysis",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "1068--1074",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, automated classification algorithms, feature selection, latent semantic analysis, linear genetic programming, real-time intrusion detection systems, signature based intrusion detection, singular value decomposition, support vector decision function, digital signatures, singular value decomposition, support vector machines",
• isbn13 = "978-1-4244-1821-3",
• file = "NN0365.pdf",
• DOI = "doi:10.1109/IJCNN.2008.4633931",
• ISSN = "1098-7576",
• abstract = "We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are able to discover latent information which allows us to design signatures for forensics and in a dual approach for real-time intrusion detection systems. The validity of this method is shown by using several automated classification algorithms (Maxim, SYM, LGP). Using the original data set we classify 99.86percent of the calls correctly. After feature extraction we classify 99.68percent of the calls correctly, while with feature selection we classify 99.78percent of the calls correctly, justifying the use of these techniques in forensics. The signatures obtained after feature selection and extraction using LSA allow us to class 95.69percent of the calls correctly with features that can be computed in real time. We use Support Vector Decision Function and Linear Genetic Programming for feature selection on a real data set generated on a live performance network that consists of probe and denial of service attacks. We find that the results reinforce our feature selection method.",
• notes = "Also known as \cite{4633931} WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Jean-Louis Lassez Ryan Rossi Stephen Sheel Srinivas Mukkamala

Citations