An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features. (1st March 2021)

• Record Type: Journal Article
• Authors: Yang, Liqun; Zhang, Jiawei; Wang, Xiaozhe; Li, Zhi; Li, Zhoujun; He, Yueying
• Is Part Of: Expert systems with applications. Volume 165(2021)
• Publication Date: 2021-03-01
• Subjects: Phishing detection -- Extreme learning machine (ELM) -- ADASYN -- SDAE -- Dimension reduction -- Non-inverse matrix; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113863
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Define three types of features extracted from URLs, domains, etc. Exploit a method to balance the majority and minority class samples. Adopt an improved DAE-based method to reduce the dimension of the dataset. Boost the detection performance by using the improved ELM-based classifier. Do experiments to verify the feasibility and effectiveness of the proposed approach. Abstract: In this paper, a novel approach based on non-inverse matrix online sequence extreme learning machine (NIOSELM) for phishing detection is presented, which takes into account three types of features to comprehensively characterize a website. For the NIOSELM algorithm, we use Sherman Morriso Woodbury equation to avoid the matrix inversion operation, and introduce the idea of online sequence extreme learning machine (OSELM) to update the training model. In order to reduce the dependence of the detection model on the majority class, we use Adaptive Synthetic Sampling (ADASYN) algorithm to generate the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. Furthermore, an improved denoising auto-encoder (SDAE) is designed to reduce the dimension of the experimental dataset. The experimental results show the efficiency and feasibility of the proposed detection mechanism. Moreover, the overall detection performance of NIOSELM is better than that of other existing methods, especially in training speed and the detection accuracy.