Over the past years, the application of deep neural networks in a wide range of areas is noticeably increasing. While many state-of-the-art deep neural networks are providing the performance comparable or in some cases even superior to humans, major challenges such as parameter settings for learning deep neural networks and construction of deep learning architectures still exist. The implications of those challenges have a significant impact on how a deep neural network is going to perform on a specific task. With the proposed method, presented in this paper, we are addressing the problem of parameter setting for a deep neural network utilizing swarm intelligence algorithms. In our experiments, we applied the proposed method variants to the classification task for distinguishing between phishing and legitimate websites. The performance of the proposed method is evaluated and compared against four different phishing datasets, two of which we prepared on our own. The results, obtained from the conducted empirical experiments, have proven the proposed approach to be very promising. By utilizing the proposed swarm intelligence based methods, we were able to statistically significantly improve the predictive performance when compared to the manually tuned deep neural network. In general, the improvement of classification accuracy ranges from 2.5% to 3.8%, while the improvement of F1-score reached even 24% on one of the datasets.

中文翻译：

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使用群智能对钓鱼网站分类的深度神经网络参数设置

在过去的几年里，深度神经网络在广泛领域的应用显着增加。尽管许多最先进的深度神经网络提供的性能与人类相当，甚至在某些情况下甚至优于人类，但学习深度神经网络的参数设置和深度学习架构的构建等主要挑战仍然存在。这些挑战的影响对深度神经网络在特定任务上的执行方式产生了重大影响。通过本文提出的方法，我们正在解决利用群体智能算法的深度神经网络的参数设置问题。在我们的实验中，我们将所提出的方法变体应用于区分网络钓鱼和合法网站的分类任务。所提出的方法的性能被评估并与四个不同的网络钓鱼数据集进行比较，其中两个是我们自己准备的。从进行的经验实验中获得的结果证明了所提出的方法是非常有前途的。通过利用所提出的基于群体智能的方法，与手动调整的深度神经网络相比，我们能够在统计上显着提高预测性能。一般来说，分类准确率的提高范围从 2.5% 到 3.8%，而 F1-score 在其中一个数据集上的提高甚至达到了 24%。通过利用所提出的基于群体智能的方法，与手动调整的深度神经网络相比，我们能够在统计上显着提高预测性能。一般来说，分类准确率的提高范围从 2.5% 到 3.8%，而 F1-score 在其中一个数据集上的提高甚至达到了 24%。通过利用所提出的基于群体智能的方法，与手动调整的深度神经网络相比，我们能够在统计上显着提高预测性能。一般来说，分类准确率的提高范围从 2.5% 到 3.8%，而 F1-score 在其中一个数据集上的提高甚至达到了 24%。