Economic Denial Security Framework Targets Cost-Based Defense for IoT Devices

Global: Economic Denial Security Framework Targets Cost-Based Defense for IoT Devices

A new framework called Economic Denial Security (EDS) seeks to make cyber attacks on Internet-of-Things (IoT) and edge devices economically infeasible by leveraging an asymmetry between defenders and attackers.

Framework Overview

The research, posted on arXiv in December 2025, proposes four complementary mechanisms—adaptive computational puzzles, decoy-driven interaction entropy, temporal stretching, and bandwidth taxation—that together amplify the cost of an attack beyond the resources required for a successful intrusion.

Theoretical Foundations

Formal analysis models the interaction as a Stackelberg game, yielding closed-form equilibria for optimal parameter selection (Theorem 1) and demonstrating that the combined mechanisms generate 2.1 × greater attacker costs than the sum of the individual components (Theorem 2).

Implementation Details

Implementation targets resource-constrained platforms, requiring less than 12 KB of memory and enabling deployment on ESP32-class microcontrollers.

Experimental Results

Experimental evaluation on a heterogeneous 20-device IoT testbed, covering four attack scenarios over 30 trials (p < 0.001), reported slowdown factors ranging from 32‑ to 560‑fold, cost asymmetries between 85‑ and 520‑to‑1, and attack-success reductions of 8‑% to 62‑% while adding under 20 ms of latency and generating near-zero false positives.

Malware Mitigation Performance

When tested against the IoT-23 malware suite (including Mirai, Torii, and Hajime), the EDS framework alone mitigated 88 % of malicious activity; integrated with a machine-learning intrusion-detection system, mitigation rose to 94 % compared with 67 % for the IDS alone, representing a 27 % improvement.

Practical Implications

The authors argue that the detection-independent nature of EDS makes it suitable for environments where traditional machine-learning based intrusion detection is impractical due to limited computational resources.

Future Directions

Future work outlined in the paper includes extending the game-theoretic model to multi-defender scenarios and exploring adaptive tuning of puzzle difficulty based on real-time network conditions.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.