Tags → #machine learning

New Metric Aims to Improve ML-NIDS Against Adversarial Attacks

Recent research introduces the Perturb-ability Score (PS) to improve the resilience of Machine Learning-based Network Intrusion Detection Systems against adversarial attacks, highlighting the importance of feature selection and pre-processing in enhancing network security.
Zero-Space Detection Framework for Ransomware Identification Introduced

The Zero-Space Detection framework represents a significant advancement in cybersecurity, utilizing advanced algorithms and behavioral analysis to enhance real-time threat response and adaptability across various cyber threats.
LLMs May Enhance Vulnerability Detection Methods

Recent research highlights the potential of large language models to enhance vulnerability detection in software development by improving accuracy and integrating with DevSecOps pipelines.
Research Examines Vulnerabilities in ResNet-50 Machine Learning Model

Recent research on the ResNet-50 image classification model highlights its vulnerabilities to adversarial attacks and emphasizes the need for robust defense mechanisms to enhance cybersecurity in critical applications.
VulSifter Enhances Vulnerability Detection in Code Commits

VulSifter is a large language model designed to improve the detection of software vulnerabilities by accurately identifying genuine vulnerability-fixing code changes and reducing false positives in reporting.
Trusted Machine Learning Models Enhance Data Privacy Solutions

The emergence of Trusted Capable Model Environments (TCMEs) is transforming data protection and privacy by enabling secure collaboration and innovative monitoring solutions in various sectors.
Adaptive Security Strategies for Large Language Models

Recent research highlights the effectiveness of adaptive defense mechanisms in large language models, emphasizing the importance of dynamic adjustments to enhance system performance and security against cyber threats.
QUADFormer Framework Enhances GPS Spoofing Detection for Quadrotors

The QUADFormer framework introduces advanced techniques for enhancing threat detection in quadrotor UAVs, emphasizing real-time response and adaptive learning to improve cybersecurity resilience.
HERA Tool Enhances Network Traffic Analysis for Cybersecurity

The exploration of HERA, an open-source tool for dataset creation, highlights its potential to enhance threat detection in cybersecurity through advanced machine learning algorithms and real-time network monitoring.
New Method Enhances Intrusion Detection System Adaptability

Recent research introduces the Strategic Sample Selection and Forgetting (SSF) approach, a continual learning method aimed at enhancing Intrusion Detection Systems (IDS) by effectively utilizing previously labeled samples to adapt to new data while maintaining detection accuracy.