Intelligent cyber-phishing detection for online
Intelligent cyber-phishing detection for online
复制标题
DOI：
10.1016/j.cose.2020.102123
复制
发表时间：
2021-02-13
影响因子：
5.6
通讯作者：
Woodward, J.
中科院分区：
文献类型：
--
作者：
Barraclough, P. A.;Fehringer, G.;Woodward, J.
网络钓鱼攻击正在增加，导致在线服务和用户的财务损失和敏感信息被盗。反网络钓鱼方法主要集中在基于黑名单的方法上，这些方法使用手动验证的统一资源定位器（URL）;或者基于内容的方法，这些方法利用基于机器学习（ML）的分类器。然而，网络欺诈仍在上升。在这项研究中，我们引入了一种新的方法，结合黑名单为基础的，基于Web内容和启发式的方法，使用ML算法与全面的功能，以允许更准确的钓鱼攻击检测。基于自适应神经模糊推理系统（ANFIS），朴素贝叶斯（NB），PART，J 48和JRip的功能进行了广泛的评估，使用评估方法（度量）来衡量所提出的方法的性能。所有分类器的准确率都在99% - 99.33%之间。PART以0.006秒的速度达到了99.33%的准确率，这是最好的性能。我们的实验表明，所提出的方法可以检测钓鱼网站，具有很高的准确性，实时和推广以及新的钓鱼攻击。与该领域的相关方法相比，所提出的方法具有最好的性能。(C)2020爱思唯尔有限公司保留所有权利。
Phishing attacks are on the increase, resulting in financial loss and theft of sensitive information to online services and users. Anti-phishing approaches have concentrated on blacklist-based approaches that use manually verified Unified Resource Locators (URLs); or content-based methods that utilise heuristics-based machine learning (ML) classifiers. However, online deception is still on the rise. In this study, we introduce a novel methodology combining blacklist-based, web content-based and heuristic based approaches, using ML algorithms with comprehensive features to allow more accurate phishing attack detection. Extensive evaluation was carried out based on Adaptive neuro-fuzzy inference system (ANFIS), Naive Bayes (NB), PART, J48, and JRip with features, using evaluation methods (metrics) to measure the proposed method performance. All the classifiers achieved over 99% - 99.33% accuracy. PART attained 99.33% accuracy with 0.006 seconds (secs) speed, which is the best performance. We experimentally demonstrate that the proposed methodology can detect phishing websites with a high accuracy in real-time and generalise well to new phishing attacks. The proposed approach has the best performance compared to related approaches in the field. (C) 2020 Elsevier Ltd. All rights reserved.