The Effect of Feature Selection on Phish Website Detection

Abstract

Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

Authors and Affiliations

Hiba Zuhair, Ali Selmat, Mazleena Salleh

Keywords

Related Articles

Multi-Agent Based Model for Web Service Composition

The evolution of the Internet and the competitiveness among companies were factors in the explosion of Web services. Web services are applications available on the Internet each performing a particular task. Web users of...

Image Enhancement Using Homomorphic Filtering and Adaptive Median Filtering for Balinese Papyrus (Lontar)

Balinese papyrus (Lontar) is one of the most popular media to write for more than a hundred years in Indonesia. Balinese papyrus are used to document things that are considered important in the past. Most of the balinese...

A Machine Vision System for Quality Inspection of Pine Nuts

Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in...

FRoTeMa: Fast and Robust Template Matching

Template matching is one of the most basic techniques in computer vision, where the algorithm should search for a template image T in an image to analyze I. This paper considers the rotation, scale, brightness and contra...

Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorith...

Download PDF file
  • EP ID EP143461
  • DOI 10.14569/IJACSA.2015.061031
  • Views 100
  • Downloads 0

How To Cite

Hiba Zuhair, Ali Selmat, Mazleena Salleh (2015). The Effect of Feature Selection on Phish Website Detection. International Journal of Advanced Computer Science & Applications, 6(10), 221-232. https://europub.co.uk/articles/-A-143461