Phishing is a fraudulent practice and a form of cyber-attack designed and executed with the sole purpose of gathering sensitive information by masquerading the genuine websites. Phishers fool users by replicating the original and genuine contents to reveal personal information such as security number, credit card number, password, etc. There are many anti-phishing techniques such as blacklist- or whitelist-, heuristic-feature- and visual-similarity-based methods proposed as of today. Modern browsers adapt to reduce the chances of users getting trapped into a vicious agenda, but still users fall as prey to phishers and end up revealing their secret information. In a previous work, the authors proposed a machine learning approach based on heuristic features for phishing website detection and achieved an accuracy of 99.5% using 18 features. In this paper, we have proposed novel phishing URL detection models using (a) Deep Neural Network (DNN), (b) Long Short-Term Memory (LSTM) and (c) Convolution Neural Network (CNN) using only 10 features of our earlier work. The proposed technique achieves an accuracy of 99.52% for DNN, 99.57% for LSTM and 99.43% for CNN. The proposed techniques utilize only one third-party service feature, thus making it more robust to failure and increases the speed of phishing detection.

网络钓鱼是一种欺诈行为，是一种网络攻击形式，其设计和执行的唯一目的是通过伪装真实的网站来收集敏感信息。网络钓鱼者通过复制原始内容和真实内容来泄露用户信息，例如安全号码，信用卡号，密码等，从而欺骗用户。有许多反网络钓鱼技术，例如黑名单或白名单，启发式功能和视觉相似性截止到今天提出的基于方法。现代浏览器会进行调整，以减少用户陷入恶意议程的机会，但用户仍然会成为网络钓鱼者的猎物，并最终泄露其秘密信息。在先前的工作中，作者提出了一种基于启发式功能的网络钓鱼网站检测机器学习方法，并使用18个功能实现了99.5％的准确性。在本文中，我们仅使用我们的10个功能，提出了使用（a）深度神经网络（DNN），（b）长期短期记忆（LSTM）和（c）卷积神经网络（CNN）的新型网络钓鱼URL检测模型。早期的工作。所提出的技术对DNN的精度为99.52％，对LSTM的精度为99.57％，对于CNN的精度为99.43％。所提出的技术仅利用一种第三方服务功能，从而使其对故障更加健壮并提高了网络钓鱼检测的速度。