Advances in Network Resilience and Adversarial Robustness

The field of network science and cybersecurity is moving towards a deeper understanding of network resilience and adversarial robustness. Recent studies have highlighted the importance of identifying critical nodes and developing monitoring and protection mechanisms for Internet infrastructure. The analysis of network topology and the detection of community structures have been shown to be crucial in understanding the behavior of networks under attack. Furthermore, the development of novel attack strategies and the evaluation of their effectiveness have underscored the need for more robust monitoring algorithms to protect against advanced attack strategies. Noteworthy papers include: Structural Resilience Analysis of an Internet Fragment Against Targeted and Random Attacks, which provides valuable insights into the importance of identifying critical nodes. Monero Peer-to-peer Network Topology Analysis, which sheds light on the core-periphery structure of the Monero network. Cluster-Aware Attacks on Graph Watermarks, which introduces a cluster-aware threat model and proposes novel attack strategies. Residual-Evasive Attacks on ADMM in Distributed Optimization, which presents two attack strategies designed to evade detection in ADMM-based systems.

Sources

Structural Resilience Analysis of an Internet Fragment Against Targeted and Random Attacks -- A Case Study Based on iThena Project Data

Monero Peer-to-peer Network Topology Analysis

Cluster-Aware Attacks on Graph Watermarks

Residual-Evasive Attacks on ADMM in Distributed Optimization

Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers

Edge-Based Learning for Improved Classification Under Adversarial Noise

Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks

RevealNet: Distributed Traffic Correlation for Attack Attribution on Programmable Networks

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