The openness feature of Twitter allows programs to generate and control Twitter accounts automatically via the Twitter API. These accounts, which are known as bots, can automatically perform actions such as tweeting, re-tweeting, following, unfollowing, or direct messaging other accounts, just like real people. They can also conduct malicious tasks such as spreading of fake news, spams, malicious software and other cyber-crimes. In this paper, we introduce a novel bot detection approach using deep learning, with the Multi-layer Perceptron Neural Networks and nine features of a bot account. A web crawler is developed to automatically collect data from public Twitter accounts and build the testing and training datasets, with 860 samples of human and bot accounts. After the initial training is done, the Multilayer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.
翻译:Twitter的开放性功能使得程序能够通过Twitter API自动生成和控制Twitter账户。这些账户被称为机器人账户,可以自动执行推特、在跟踪、跟踪或直接传递其他账户等行动,就像真实人物一样。它们也可以执行恶意任务,如传播假消息、垃圾邮件、恶意软件和其他网络犯罪。在本文中,我们引入了一种利用深层次学习的新颖的机器人检测方法,包括多层 Perphen神经网络和一个机器人账户的9个功能。 开发了一个网络爬行器,自动从公共推特账户收集数据,并用860个人类和机器人账户样本建立测试和培训数据集。 初步培训完成后,多层 Perphen神经网络实现了92%的总体准确率,这证明了拟议方法的绩效。