Online Social Networks (OSNs) are witnessing sophisticated cyber threats, that are generally conducted using fake or compromised profiles. Automated agents (aka socialbots), a category of sophisticated and modern threat entities, are the native of the social media platforms and responsible for various modern weaponized information-related attacks, such as astroturfing, misinformation diffusion, and spamming. Detecting socialbots is a challenging and vital task due to their deceiving character of imitating human behavior. To this end, this paper presents an attention-aware deep neural network model, DeepSBD, for detecting socialbots on OSNs. The DeepSBD models users' behavior using profile, temporal, activity, and content information. It jointly models OSN users' behavior using Bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures. It models profile, temporal, and activity information as sequences, which are fed to a two-layers stacked BiLSTM, whereas content information is fed to a deep CNN. We have evaluated DeepSBD over five real-world benchmark datasets and found that it performs significantly better in comparison to the state-of-the-arts and baseline methods. We have also analyzed the efficacy of DeepSBD at different ratios of socialbots and benign users and found that an imbalanced dataset moderately affects the classification accuracy. Finally, we have analyzed the discrimination power of different behavioral components, and it is found that both profile characteristics and content behavior are most impactful, whereas diurnal temporal behavior is the least effective for detecting socialbots on OSNs.

中文翻译：

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DeepSBD：一种用于社交机器人检测的具有注意力机制的深度神经网络模型


在线社交网络 (OSN) 正在目睹复杂的网络威胁，这些威胁通常是使用虚假或受损的个人资料进行的。自动化代理（又名社交机器人）是一类复杂的现代威胁实体，是社交媒体平台的原生者，负责各种现代武器化信息相关攻击，例如 astroturfing、错误信息传播和垃圾邮件。由于社交机器人模仿人类行为的欺骗性，检测社交机器人是一项具有挑战性且至关重要的任务。为此，本文提出了一种注意力感知深度神经网络模型 DeepSBD，用于检测 OSN 上的社交机器人。 DeepSBD 使用个人资料、时间、活动和内容信息对用户的行为进行建模。它使用双向长短期记忆 (BiLSTM) 和卷积神经网络 (CNN) 架构联合建模 OSN 用户的行为。它将个人资料、时间和活动信息建模为序列，并将其馈送到两层堆叠的 BiLSTM，而内容信息则馈送到深度 CNN。我们在五个现实世界基准数据集上评估了 DeepSBD，发现与最先进的方法和基线方法相比，它的性能明显更好。我们还分析了 DeepSBD 在不同比例的社交机器人和良性用户下的效果，发现不平衡的数据集会适度影响分类准确性。最后，我们分析了不同行为成分的辨别力，发现个人资料特征和内容行为都最具影响力，而昼间时间行为对于检测 OSN 上的社交机器人效果最差。