An efficient detection of TCP Syn flood attacks with spoofed IP addresses

Bae, Young-Jun; Kim, Intae; Hwang, Seong Oun

doi:10.3233/jifs-169839

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“…Applications of keyed learning fall within the scope of exploratory adversarial learning [2]. This context is generally appropriate for anomaly detection, which comprises several application domains, including intrusion detection [3,5,6,14,25,33,34], attack and malware analysis [7,16,[35][36][37], defacement response [8,17,38,39], Web promotional infection detection [40], and biometric and continuous user authentication [11,18].…”

Section: Applicationsmentioning

confidence: 99%

Keyed learning: An adversarial learning framework—formalization, challenges, and anomaly detection applications

Bergadano

2019

ETRI Journal

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We propose a general framework for keyed learning, where a secret key is used as an additional input of an adversarial learning system. We also define models and formal challenges for an adversary who knows the learning algorithm and its input data but has no access to the key value. This adversarial learning framework is subsequently applied to a more specific context of anomaly detection, where the secret key finds additional practical uses and guides the entire learning and alarm‐generating procedure.

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