Tags → #machine learning

AI Innovations Enhance Cybersecurity Software Quality and Security

Recent research highlights innovative methodologies that utilize Artificial Intelligence to enhance vulnerability detection and prioritization in cybersecurity.
Automating Code Vulnerability Detection Using GitHub Issues

A recent study highlights the potential of transformer-based models for automated vulnerability detection in cybersecurity, emphasizing their integration into CI/CD pipelines to enhance early threat identification.
PromptKeeper Enhances Security for AI System Prompts

The article discusses the critical intersection of cybersecurity and machine learning, highlighting the need for secure API development and data privacy in light of emerging vulnerabilities and innovative protective strategies.
TrojanDec Framework Enhances Detection of Trojan Inputs

Recent research introduces the TrojanDec framework, which utilizes advanced techniques to improve detection and restoration processes against trojan attacks within reinforcement learning environments.
Advancements in Encrypted Traffic Classification with MH-Net

Recent research into MH-Net has introduced advancements in classifying encrypted network traffic, utilizing byte correlation analysis and multi-task training techniques to improve real-time monitoring and reduce false positives.
Machine Learning Techniques in Cybersecurity and Digital Forensics

Recent research highlights the effectiveness of machine learning techniques, particularly Support Vector Machines, in enhancing real-time threat detection and automated digital forensics within cybersecurity.
PhishAgent Introduces Advanced System for Phishing Detection

PhishAgent is a new multimodal phishing detection system designed to enhance cybersecurity defenses through advanced detection mechanisms and real-time adaptability.
AI and Machine Learning Enhance Smart Contract Security

Recent research explores the integration of artificial intelligence with blockchain technologies to enhance smart contract security and improve real-time vulnerability detection.
Framework Developed for Smart Contract Vulnerability Detection

Researchers have introduced the AF-STip framework, which utilizes graph-based methodologies and attention mechanisms to enhance the accuracy and interpretability of smart contract vulnerability detection in blockchain technology.
Large Language Models Used to Identify Malicious npm Packages

Recent research emphasizes the importance of collaboration between developers and security teams in enhancing defenses against sophisticated software supply chain attacks through innovative tools and methodologies.