Deep Learning Models for Detecting Online Harmful Content

Abstract

Deep learning (DL) has emerged as a transformative technology with substantial impact across various domains, including cybersecurity. This dissertation leverages deep learning methods and their applications to address increasingly sophisticated cyber threats. DL methods are capable of learning complex and abstract features from large-scale data, making them well-suited for identifying and mitigating cyber threats that traditional methods might miss. This dissertation focuses on the practical implementation and evaluation of DL models for detecting real-world cybersecurity threats, namely, clickbait, Twitter bots and SMS spam. Specifically, we propose: a novel attention-based neural network model named Knowledge-Enhanced Clickbait Detector (KED) that uses linguistic knowledge graphs built from WordNet to guide the attention mechanisms. The proposed neural network can effectively capture discriminative features from local and global similarities via the proposed knowledge-enhanced attention mechanisms. Moreover, we incorporate human semantic knowledge into the neural network and its attention mechanisms to better capture semantic correlations of headline-article word pairs. a novel recurrent neural network (RNN) model to distinguish Twitter bots from human accounts based on textual content of their tweets. We use several types of linguistic embeddings to encode tweets, namely, word embeddings, character embeddings, part-of-speech embeddings, and named-entity embeddings. We avoid using handcrafted features, which require time-consuming and labor-intensive feature engineering. This advantage allows for faster and easier implementation and deployment of the bot detection scheme. a novel lightweight deep neural model called Lightweight Gated Recurrent Unit (LGRU) for SMS spam detection. We incorporate enhancing semantics retrieved from external knowledge to assist in understanding SMS text inputs for more accurate detection. In addition, the lightweight model illustrates a method to minimize unnecessary complexity in training recurrent models without compromising the performance, which we believe is applicable to many other complex recurrent models for other applications. Experimental results show that the above models outperform their counterparts, including state-of-the-art models/systems and other baseline models, in terms of predictive performance and/or running time. The proposed models provide robust, scalable, and real-time security solutions that can adapt to the rapidly changing landscape of cyber threats.