论文标题
多层感知器神经网络，用于改善恶意网络钓鱼URL的检测性能，而不会影响其他攻击类型分类
Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification
论文作者
论文摘要
这里的假设指出，诸如多层感知器（MLP）等神经网络算法在区分恶意和半结构的网络钓鱼URL方面具有更高的精度。与经典的机器学习算法（例如逻辑回归和多项式幼稚贝叶斯）相比，经典算法在很大程度上依赖于实质性的语料库数据培训和机器学习专家的领域知识来执行复杂的功能工程。 MLP可以执行非线性可分离多类分类，而较少专注于语料库特征培训。此外，返回权重调整可以了解哪些功能在将网络钓鱼与其他攻击类型区分开时更为重要。
The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and semi-structured phishing URLs. Compared to classical machine learning algorithms such as Logistic Regression and Multinomial Naive Bayes, the classical algorithms rely heavily on substantial corpus data training and machine learning experts' domain knowledge to perform complex feature engineering. MLP could perform non-linear separable multi-classes classification and focus less on corpus feature training. In addition, backpropagation weight adjustment could learn which features are more important in differentiating phishing from other attack types.
