Voice Cloning employs technology and algorithms to create an artificial or synthetic reproduction of a person's voice. To understand and mimic the distinct vocal qualities of the target speaker, including tone, pi...
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As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a com...
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In contemporary society, Suicide has become a notable concern within the realm of public health. Suicidal ideation (SI) encompasses thoughts about ending one's life. Factors contributing to suicidal thoughts and a...
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Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulti...
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ISBN:
(纸本)9798350362480
Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur
Smart buildings have been getting a lot of interest because of their potential to improve energy efficiency and maximize resource usage. Incorporating software-defined networks (SDN), machine learning methods, and air...
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There are many deepfake detection models available, each with their own limitations. This research concentrates on one such limitation and addresses it. That research gap would be the inefficient behavior of the deepf...
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Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security probl...
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Ensuring regulatory compliance and maintaining client trust in the legal industry depend on the safe transfer and preservation of sensitive data. Maintaining the safety and security of confidential information is esse...
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The self-rationalising capabilities of large language models (LLMs) have been explored in restricted settings, using task-specific data sets. However, current LLMs do not (only) rely on specifically annotated data;non...
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This research-based project is about a new way to put feelings into computer-generated speech. It has two stages: text emotion detection and emotional speech synthesis. In the former part, labeled text data is used to...
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