COPYRIGHT YEAR

2016

AUTHORS

Hyunsuk Chung, Byeong Ho Kang, Soyeon Caren Han, Renjie Chen

TITLE

Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction

ABSTRACT

Detecting phishing websites has been noted as complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. We propose a novel approach that uses Ripple-down Rule (RDR) to acquire knowledge from human experts with the modified RDR model-generating algorithm (Induct RDR), which applies machine-learning approach. The modified algorithm considers two different data types (numeric and nominal) and also applies information theory from decision tree learning algorithms. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and over-fitting problems of machine learning approach. Three models were included in comparison: RDR with machine learning and human knowledge, RDR machine learning only and J48 machine learning only. The result shows the improvements in prediction accuracy of the knowledge acquired by machine learning.

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


27 TRIPLES      24 PREDICATES      24 URIs      13 LITERALS

Subject Predicate Object
1 book-chapters:1f4f6a28fc3390c1574afe85cd6d4663 sg:abstract Abstract Detecting phishing websites has been noted as complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. We propose a novel approach that uses Ripple-down Rule (RDR) to acquire knowledge from human experts with the modified RDR model-generating algorithm (Induct RDR), which applies machine-learning approach. The modified algorithm considers two different data types (numeric and nominal) and also applies information theory from decision tree learning algorithms. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and over-fitting problems of machine learning approach. Three models were included in comparison: RDR with machine learning and human knowledge, RDR machine learning only and J48 machine learning only. The result shows the improvements in prediction accuracy of the knowledge acquired by machine learning.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
4 sg:bodyHtmlRights Restricted
5 sg:bodyPdfRights Restricted
6 sg:copyrightHolder Springer International Publishing Switzerland
7 sg:copyrightYear 2016
8 sg:ddsId Chap7
9 sg:doi 10.1007/978-3-319-42911-3_7
10 sg:esmRights OpenAccess
11 sg:hasBook books:29f876c32aef3d9ef736f460aa69e5fc
12 sg:hasBookEdition book-editions:41f1a7703710ba7769d343b10145600a
13 sg:hasContribution contributions:0a6402b2fca9042f765720628ad24453
14 contributions:2c1ad3de1ff0c1deb2733143a948b4c1
15 contributions:40cbbe3663f1dac2559bbab7ea83510a
16 contributions:ba00aff4891fd9bc07f4ec6671b628c8
17 sg:language En
18 sg:license http://scigraph.springernature.com/explorer/license/
19 sg:metadataRights OpenAccess
20 sg:pageFirst 80
21 sg:pageLast 92
22 sg:scigraphId 1f4f6a28fc3390c1574afe85cd6d4663
23 sg:title Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction
24 sg:webpage https://link.springer.com/10.1007/978-3-319-42911-3_7
25 rdf:type sg:BookChapter
26 rdfs:label BookChapter: Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction
27 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-319-42911-3_7
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/1f4f6a28fc3390c1574afe85cd6d4663'

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/1f4f6a28fc3390c1574afe85cd6d4663'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/book-chapters/1f4f6a28fc3390c1574afe85cd6d4663'

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

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/book-chapters/1f4f6a28fc3390c1574afe85cd6d4663'






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


...