COPYRIGHT YEAR

2015

AUTHORS

Charles LeDoux, Arun Lakhotia

TITLE

Malware and Machine Learning

ABSTRACT

Malware analysts use Machine Learning to aid in the fight against the unstemmed tide of new malware encountered on a daily, even hourly, basis. The marriage of these two fields (malware and machine learning) is a match made in heaven: malware contains inherent patterns and similarities due to code and code pattern reuse by malware authors; machine learning operates by discovering inherent patterns and similarities. In this chapter, we seek to provide an overhead, guiding view of machine learning and how it is being applied in malware analysis. We do not attempt to provide a tutorial or comprehensive introduction to either malware or machine learning, but rather the major issues and intuitions of both fields along with an elucidation of the malware analysis problems machine learning is best equipped to solve.

Related objects

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

Download the RDF metadata as:   json-ld nt turtle xml License info


20 TRIPLES      19 PREDICATES      21 URIs      11 LITERALS

Subject Predicate Object
1 book-chapters:f2f892e55e3d7aff3355faa72600f5f1 sg:abstract Abstract Malware analysts use Machine Learning to aid in the fight against the unstemmed tide of new malware encountered on a daily, even hourly, basis. The marriage of these two fields (malware and machine learning) is a match made in heaven: malware contains inherent patterns and similarities due to code and code pattern reuse by malware authors; machine learning operates by discovering inherent patterns and similarities. In this chapter, we seek to provide an overhead, guiding view of machine learning and how it is being applied in malware analysis. We do not attempt to provide a tutorial or comprehensive introduction to either malware or machine learning, but rather the major issues and intuitions of both fields along with an elucidation of the malware analysis problems machine learning is best equipped to solve.
2 sg:copyrightHolder Springer International Publishing Switzerland
3 sg:copyrightYear 2015
4 sg:ddsId Chap1
5 sg:doi 10.1007/978-3-319-08624-8_1
6 sg:hasBook books:8bb933d8d99691b11dc9387c8f63c5a6
7 sg:hasBookEdition book-editions:bccf9d3bfcfdc461aa1424d8dd416cf2
8 sg:hasContributingOrganization grid-institutes:grid.266621.7
9 sg:hasContribution contributions:35e213646db8ce5341b73e3a4342e953
10 contributions:7adb8dd5f234509bacaf5df6fbf025ac
11 sg:language En
12 sg:license http://scigraph.springernature.com/explorer/license/
13 sg:pageFirst 1
14 sg:pageLast 42
15 sg:scigraphId f2f892e55e3d7aff3355faa72600f5f1
16 sg:title Malware and Machine Learning
17 sg:webpage https://link.springer.com/10.1007/978-3-319-08624-8_1
18 rdf:type sg:BookChapter
19 rdfs:label BookChapter: Malware and Machine Learning
20 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-319-08624-8_1
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/book-chapters/f2f892e55e3d7aff3355faa72600f5f1'

N-Triples is a line-based linked data format ideal for batch operations .

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/book-chapters/f2f892e55e3d7aff3355faa72600f5f1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/book-chapters/f2f892e55e3d7aff3355faa72600f5f1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/book-chapters/f2f892e55e3d7aff3355faa72600f5f1'






Preview window. Press ESC to close (or click here)


...