CyberdeceptionAtlas · Question → testES

Library / Paper

Paper

Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET

The paper describes the use of honeypot agents that generate Route Request (RREQ) packets to attract and entrap black hole attackers in mobile ad hoc networks (MANETs), together with a deep learning technique based on Long-Short Term Memory (LSTM). The authors evaluate the proposed scheme, named HPAS-LSTM, through ns-2 simulations and compare its results with other black hole detection methods in terms of throughput, packet loss rate, packet delivery ratio and total network delay.

HoneypotNetwork

What we know

The paper describes the use of honeypot agents that generate Route Request (RREQ) packets to attract and entrap black hole attackers in mobile ad hoc networks (MANETs), together with a deep learning technique based on Long-Short Term Memory (LSTM). The authors evaluate the proposed scheme, named HPAS-LSTM, through ns-2 simulations and compare its results with other black hole detection methods in terms of throughput, packet loss rate, packet delivery ratio and total network delay.

Evidence limits

Review based only on the Crossref-deposited abstract; the full text was not examined and the results were not independently validated.

Reviewed: author abstract. Full-text results were not analyzed.

Sources and provenance

  1. Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET
    abstract · 2026-10-11

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

RIS · BibTeX