Paper Info
Title
Phishing Emails Detection Using Cs-Svm
Abstract
Phishing attacks are common online, which have resulted in financial losses through using either malware or social engineering. Thus, phishing email detection with high accuracy has been an issue of great interest. Machine learning-based detection methods, particularly Support Vector Machine (SVM), have been proved to be effective. However, the parameters of kernel method, whose default is that class numbers reciprocals in general, affect the classification accuracy of SVM. In order to improve the classification accuracy, this paper proposes a model, called Cuckoo Search SVM(CS-SVM). The CS-SVM extracts 23 features, which are used to construct the hybrid classifier. In the hybrid classifier, Cuckoo Search (CS) is integrated with SVM to optimize parameter selection of Radial Basis Function(RBF). Experiments are performed on a dataset consisting of 1,384 phishing emails and 20,071 non-phishing emails. Experimental results show that the proposed method has higher phishing email detection accuracy than SVM classifier with default parameter value. The CS-SVM classifier can obtain the highest accuracy of 99.52 percent.
Year
DOI
Venue
2017
10.1109/ISPA/IUCC.2017.00160
2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017)
Keywords
Field
DocType
APT, phishing email detection, SVM, Cuckoo search, RBF
Kernel (linear algebra),Pattern recognition,Phishing,Computer science,Support vector machine,Cuckoo search,Feature extraction,Human–computer interaction,Artificial intelligence,Kernel method,Malware,Classifier (linguistics)
Conference
ISSN
Citations 
PageRank 
2158-9178
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Wei-na Niu183.26
Xiaosong Zhang200.34
Guowu Yang3206.39
Zhiyuan Ma4279.26
Zhongliu Zhuo532.09