利用可解释人工智能调查网络钓鱼的易感性

Future Internet Pub Date : 2024-01-17 DOI:10.3390/fi16010031
Zhengyang Fan, Wanru Li, Kathryn B. Laskey, Kuo-Chu Chang
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引用次数: 0

摘要

网络钓鱼攻击是数字世界中一个日益严重的威胁,影响着全球的个人和组织。了解影响网络钓鱼易感性的各种因素对于制定更有效的策略来应对这一普遍存在的网络安全挑战至关重要。机器学习已成为研究网络钓鱼易感性的一种普遍方法。该领域的大多数研究都采用了两种方法中的一种:要么探索各种因素与易感性之间的统计关联,要么使用深度神经网络等复杂模型来预测网络钓鱼行为。然而,这些方法在为个人提供避免未来网络钓鱼攻击的实用见解以及提供有关其网络钓鱼易感性的个性化解释方面存在局限性。在本文中,我们提出了一种机器学习方法,利用可解释的人工智能技术来研究人类和人口因素对网络钓鱼攻击易感性的影响。机器学习模型的准确率为 78%,召回率为 71%,精确率为 57%。我们的分析表明,冲动性和自觉性等心理因素以及适当的在线安全习惯会显著影响个人对网络钓鱼攻击的易感性。此外,考虑到每个人的具体情况,我们的个性化个案分析方法可提供个性化建议,以降低遭受网络钓鱼攻击的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.
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