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HICSS
2003
IEEE
220views Biometrics» more  HICSS 2003»
15 years 11 months ago
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
This paper describes a novel approach using Hidden Markov Models (HMM) to detect complex Internet attacks. These attacks consist of several steps that may occur over an extended pe...
Dirk Ourston, Sara Matzner, William Stump, Bryan H...
AIME
1997
Springer
15 years 10 months ago
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244...
William Rodman Shankle, Subramani Mani, Michael J....
ACL
2006
15 years 7 months ago
Morphological Richness Offsets Resource Demand - Experiences in Constructing a POS Tagger for Hindi
In this paper we report our work on building a POS tagger for a morphologically rich language- Hindi. The theme of the research is to vindicate the stand that- if morphology is st...
Smriti Singh, Kuhoo Gupta, Manish Shrivastava, Pus...
BMCBI
2008
87views more  BMCBI 2008»
15 years 6 months ago
Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information
Background: As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational method...
Jing Hu, Changhui Yan
JMLR
2006
132views more  JMLR 2006»
15 years 6 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof