The field of cybersecurity is moving towards the development of more advanced and effective threat detection and prevention methods. This is driven by the increasing complexity and sophistication of cyber attacks, which require innovative solutions to stay ahead of the threats. One of the key areas of focus is the use of large language models (LLMs) and machine learning algorithms to improve the accuracy and efficiency of threat detection. Another important aspect is the creation of more comprehensive and realistic datasets to support research and development in this area. Noteworthy papers in this regard include Insight-LLM, which presents a modular multi-view fusion framework for insider threat detection, and E-PhishGen, which proposes an LLM-based framework to generate novel phishing-email datasets. Additionally, Phish-Blitz introduces a tool for comprehensive webpage resource collection and visual integrity preservation to improve phishing detection accuracy.
Cybersecurity Threat Detection and Prevention
Sources
Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection
E-PhishGen: Unlocking Novel Research in Phishing Email Detection
Linguistic Hooks: Investigating The Role of Language Triggers in Phishing Emails Targeting African Refugees and Students
ForensicsData: A Digital Forensics Dataset for Large Language Models
An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection
Phish-Blitz: Advancing Phishing Detection with Comprehensive Webpage Resource Collection and Visual Integrity Preservation
Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques