Incremental Adversarial Learning for Polymorphic Attack Detection

Incremental Adversarial Learning for Polymorphic Attack Detection

AI-based Network Intrusion Detection Systems (NIDS) provide effective mechanisms for cybersecurity analysts to gain insights and thwart several network attacks. Although current IDS can identify known/typical attacks with high accuracy, current research shows that such systems perform poorly when fa...

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Bibliographic Details
Main Authors: Ulya Sabeel, Shahram Shah Heydari, Khalil El-Khatib, Khalid Elgazzar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Artificial intelligence
atypical attacks
class imbalance
feature profile
intrusion detection system
polymorphic attacks
Online Access:https://ieeexplore.ieee.org/document/10570491/
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https://ieeexplore.ieee.org/document/10570491/

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