NDLI: Applying randomized projection to aid prediction algorithms in detecting high-dimensional rogue applications

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Applying randomized projection to aid prediction algorithms in detecting high-dimensional rogue applications

Content Provider	ACM Digital Library
Author	Atkison, Travis
Abstract	This paper describes a research effort to improve the use of the cosine similarity information retrieval technique to detect unknown, known or variances of known rogue software by applying the feature extraction technique of randomized projection. Document similarity techniques, such as cosine similarity, have been used with great success in several document retrieval applications. By following a standard information retrieval methodology, software, in machine readable format, can be regarded as documents in the corpus. These "documents" may or may not have a known rogue functionality. The query is software, again in machine readable format, which contains a certain type of rogue software. This methodology provides an ability to search the corpus with a query and retrieve/identify potentially rogue software as well as other instances of the same type of vulnerability. This retrieval is based on the similarity of the query to a given document in the corpus. To overcome what is known as the 'the curse of dimensionality' that can occur with the use of this type of information retrieval technique, randomized projections are used to create a low-order embedding of the high-dimensional data. For our experiment, we obtain Microsoft Windows applications, infect a subset of them with several common Trojans and apply our dimensionality and prediction methodology. Preliminary results show promise when applying randomized projections to cosine similarity in both speed of prediction and efficiency of required space when compared with using only cosine similarity.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781605584218
DOI	10.1145/1566445.1566477
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-03-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cosine similarity N-gram analysis Randomized projections Rogue software detection Information retrieval
Content Type	Text
Resource Type	Article

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