AI 용어집
인공지능 완전 사전
Intrusion Detection System (IDS)
Network or host monitoring system using ML algorithms to automatically identify malicious activities or security policy violations in real-time.
False Positive Rate
Critical metric measuring the proportion of legitimate activities incorrectly classified as malicious by the detection system, directly impacting operational efficiency.
Network Traffic Analysis
In-depth examination of network data flows using ML techniques to identify abnormal patterns indicating intrusion attempts or compromise.
Behavioral Analysis
ML approach based on establishing normal behavioral profiles for users and systems, enabling real-time detection of suspicious deviations.
Zero-day Attack Detection
Capability of ML systems to identify previously unknown threats by detecting anomalous behaviors without relying on pre-existing signatures.
Deep Learning IDS
Intrusion detection system using deep neural networks to model complex relationships in security data and improve detection accuracy.
Supervised Learning for IDS
Machine learning approach using labeled data (normal/attacks) to train classifiers capable of recognizing known intrusion attempts.
Unsupervised Learning for IDS
ML method automatically identifying anomalies without labeled training data, particularly effective against zero-day attacks and new threats.
Real-time Threat Classification
ML process instantly categorizing security events according to their danger level and attack type for appropriate and immediate response.
Ensemble Methods for Security
Combination of multiple ML algorithms to improve robustness and accuracy of intrusion detection by reducing individual model biases.
Time Series Analysis in Cybersecurity
Application of ML techniques on temporal sequences of network data to detect evolving trends and progressive or persistent attacks.
Pattern Recognition in IDS
Automatic identification of recurring patterns in security data signaling malicious activities, using advanced ML classification algorithms.
Adaptive Learning Systems
ML systems capable of continuously modifying their detection models based on new data to adapt to evolving attack techniques.
Malware Detection ML
Use of machine learning algorithms to identify malicious software based on their behavior rather than traditional signatures.
Botnet Detection
Specialized ML techniques for identifying networks of compromised machines communicating with command and control servers for malicious activities.
Threat Intelligence Integration
Incorporation of external threat data into ML models to enrich detection with contextual information about known attacks.
Feature Selection for IDS
Processus ML optimisant la sélection des variables les plus discriminantes pour réduire la complexité computationnelle tout en maximisant la précision de détection.