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A classification-by-retrieval framework for few-shot anomaly detection to detect API injection
Udi Aharon
,
Ran Dubin
,
Amit Dvir
,
Chen Hajaj
Department of Computer and Software Engineering
Department of Industrial Engineering and Management
Ariel University
Research output
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Contribution to journal
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Article
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peer-review
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Dive into the research topics of 'A classification-by-retrieval framework for few-shot anomaly detection to detect API injection'. Together they form a unique fingerprint.
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Keyphrases
Application Programming Interface
100%
Retrieval Framework
100%
Few-shot Object Detection
100%
Classification-by-Retrieval
100%
Attacker
25%
Machine Learning Models
12%
Detection Accuracy
12%
Approximate Nearest Neighbor Search
12%
Language Model
12%
Unauthorized Use
12%
Anomaly Detection System
12%
Unsupervised Anomaly Detection
12%
Computational Intelligence Techniques
12%
Gaining Access
12%
Online Systems
12%
Retrieval Approach
12%
Attack Detection
12%
Security Needs
12%
Malicious Use
12%
Injection Attacks
12%
Generic Language
12%
Lightweight Classifier
12%
FastText Embeddings
12%
Computer Science
Information Retrieval
100%
Anomaly Detection
100%
Application Programming Interface
100%
Attackers
25%
Artificial Intelligence
12%
Neighbor Search
12%
Language Modeling
12%
Classification Models
12%
Detection Accuracy
12%
Unsupervised Anomaly Detection
12%
Online System
12%
Normal Application
12%