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Deep reinforcement learning for building honeypots against runtime DoS attack

The linked preprint abstract describes DARLH, a honeypot system that uses deep reinforcement learning-based and deep recurrent neural network-based IDS agents to detect internal and external runtime DoS attacks. Its purpose is to provide dynamic intrusion detection and event validation for monitoring DoS attacks in the honeypot environment.

HoneypotDecoyNetwork

What we know

The linked preprint abstract describes DARLH, a honeypot system that uses deep reinforcement learning-based and deep recurrent neural network-based IDS agents to detect internal and external runtime DoS attacks. Its purpose is to provide dynamic intrusion detection and event validation for monitoring DoS attacks in the honeypot environment.

Evidence limits

The journal record has no abstract; this summary is based solely on the explicitly linked preprint abstract, without full-text reading or independent result validation.

Reviewed: public page. Full-text results were not analyzed.

Sources and provenance

  1. Deep reinforcement learning for building honeypots against runtime DoS attack
    source-page · 2026-10-16

Reviewed: 2026-10-16. This record may change when new evidence is found.

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