Performance Comparison of Caching Algorithms in Named Data Networks with a Focus on Mitigating Pollution Attacks and Optimizing the β Parameter

Subject Areas : New technologies in distributed systems and algorithmic computing

Atefeh Vaez shahrestani ^{1 *} , Mohammad reza Khayyambashi ² , Faramarz Safi ³

1 -
2 - Department of Computer Architecture ,Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
3 - Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Received: 2025-06-05 Accepted : 2025-07-10 Published : 2025-09-08

Keywords: Named Data Networks, Cache, Cache Hit Rate, Cache Pollution Percentage, ADMM Algorithm, ADMM-Enhanced,

Abstract :

With the rapid evolution of content-centric architectures like Named Data Networks (NDN), intelligent cache management has become vital for enhancing network performance and user experience. A key factor in this domain is the β parameter, which adjusts cache sensitivity to content popularity distribution. An inappropriate β value can reduce cache hit rates, increase data pollution, and waste resources. This research introduces a novel framework utilizing the ADMM (Directed Augmented Lagrange Multiplier Method) algorithm for distributed and adaptive tuning of the β parameter. The decentralized structure of the model enables implementation at individual network nodes, achieving fast convergence and precise β adjustments through coordinated updates of primary and dual variables. An enhanced version, ADMM-Enhanced, further improves performance by automatically optimizing λ and ρ parameters, delivering greater stability, accuracy, and adaptability to network dynamics. To assess its effectiveness, the model was tested across four topologies—simple, hierarchical, enhanced, and mesh—under three scenarios: steady-state, cache pollution attacks (CPA), and varying traffic loads. Compared with baseline algorithms (LRU, LFU-DA, PFP-DA, and PFP-β-DA), ADMM-Enhanced improved cache hit rates by 30% and fully eliminated pollution in steady-state conditions. Under a severe CPA scenario with 30% attacker nodes, it maintained a cache hit rate (CHR) and cache pollution rate (CPR) of 18%, indicating strong resilience and adaptability. In complex topologies like mesh networks, the algorithm preserved cache performance even under distributed attacks, congestion, and link failures. Overall, ADMM-Enhanced proved to be a highly adaptable and efficient solution for dynamic and threat-prone NDN environments.

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