基于在线社交媒体分析的网络欺凌和网络跟踪攻击缓解深度学习模型设计

S. Kahate, A. D. Raut
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引用次数: 0

摘要

针对实时用例识别网络欺凌和网络跟踪是一项多领域任务,涉及社交媒体数据提取、情感分析、情感模式评估和回归模型的设计。为了完成这项任务,研究人员提出了使用高密度特征表示模型来提取社交媒体情感,并将其与用户特定参数(如年龄、性别、发布时间等)相结合。但是,现有的模型要么不全面,要么在用于实时场景时只能达到有限的准确性。此外,这些模型对多模态输入不灵活,这进一步限制了它们的可伸缩性水平。为了解决这些问题,本文提出了一个深度学习模型,通过社交媒体分析来缓解网络欺凌和网络跟踪攻击。该模型首先收集用户发布的推文,提取元数据,并分析语言特征,用于训练基于长短期记忆(LSTM)的卷积神经网络(CNN),该网络有助于对推文进行预过滤。过滤后的推文通过自然语言处理(NLP)引擎传递,该引擎有助于对这些文本进行情感识别。情感数据和词嵌入功能用于预测网络欺凌和网络跟踪攻击。这是通过基于CNN的模式分析完成的,有助于有效识别和缓解这些攻击。由于这些模型的集成,该方法能够将攻击检测准确率提高3.5%,同时将实时场景下的识别延迟降低8.3%。
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Design of a Deep Learning Model for Cyberbullying and Cyberstalking Attack Mitigation via Online Social Media Analysis
Identification of cyberbullying and cyberstalking for real-time use cases is a multi domain task that involves the design of social media data extraction, sentiment analysis, sentiment pattern evaluation, and regression models. To perform this task, researchers have proposed the use of high-density feature representation models that can extract social media sentiments, and combine them with user specific parameters like age, gender, time of post, etc. But existing models are either non-comprehensive or capable of achieving limited accuracy when used for real-time scenarios. Moreover, these models are not flexible to multimodal inputs, which further limits their scalability levels. To address these concerns, this paper proposes the development of a deep learning model for cyberbullying and cyberstalking attack mitigation via social media analysis. The proposed model initially collects tweets posted by users, extracts meta data, and analyzes language features for training a Long-Short-Term Memory (LSTM) based Convolutional Neural Network (CNN), which assists in the pre-filtering of tweets. The filtered tweets are passed through a Natural Language Processing (NLP) engine that assists in sentiment identification for these texts. Sentiment data and Word Embedding capabilities are used to anticipate cyberbullying and cyberstalking attacks. This is done via CNN based pattern analysis, which assists in the efficient identification and mitigation of these attacks. Due to the integration of these models, the proposed method is able to improve attack detection accuracy by 3.5 %, while reducing the identification delay by 8.3 % in real-time scenarios.
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