Optimizing multi-objective strategies for enhanced Tor De-anonymization

REVS-T: Trust-Tier-Aware Provider Selection for Secure Vehicular Computation Offloading

Vehicular computation offloading (VCOff) enables resource-constrained vehicles to delegate delay-sensitive tasks to nearby providers. However, it remains vulnerable to strategic malicious nodes that withhold results, or behave intermittently to evade detection. Although reputation values evolve across repeated interactions, long-term security depends on how these signals are governed and enforced at the decision layer. This paper introduces REVS-T, a four-tier governance and tieraware selection mechanism using reputation bands, warningstreak escalation, and pool partitioning. Under persistent attack at 50% adversarial ratio, REVS-T achieves 91.9% task success and 96.7% malicious avoidance with zero false exclusions, outperforming Threshold by 5.5% and Beta by 14.9%. A four-step ablation under shared reputation-update logic shows composite scoring and four-tier governance as the dominant drivers, with ST-conditioned initialization providing phase-shift adaptation and a fairness guarantee no evaluated baseline achieves.

URP-FL: Robust and Personalized Federated Learning under Heterogeneous and Adversarial Conditions

Federated learning is appealing for privacy-sensitive network systems, yet its practical deployment remains hindered by the following three recurring challenges: (1) client drift under non-IID data, (2) vulnerability to corrupted updates, and (3) the communication cost of repeated model exchange. Most existing approaches address these issues in isolation. While analytically convenient, this separation often fails to reflect real-world conditions. For instance, defenses against poisoning may suppress useful updates, while personalization and compression can alter the aggregation geometry itself. In this paper, we study these effects jointly and propose URP-FL, a compact training framework that integrates reliability-aware aggregation, local regularization for drift control, and sparse client uploads. We provide theoretical analysis establishing a convergence bound with distinct terms capturing optimization error, data heterogeneity, and adversarial impact. Experiments on a non-IID image classification benchmark with sign-flip and label-flip attacks demonstrate the benefits of the unified design. Compared to FedAvg and FedProx, this URP-FL maintains accuracy under attack while reducing transmitted parameters by approximately 75%. Rather than presenting a production ready system, it offers a reproducible and technically coherent step toward federated learning that is more robust under realistic conditions.

Multi-Objective Privacy-Preserving Function Placement in an Edge-Cloud FaaS Marketplace

Improving resistance to Sybil attacks in decentralized networks using the RTCT protocol

Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning

Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rules, such as Krum and Multi-Krum, select updates that are closest to the majority under the assumption that benign updates form a compact cluster. However, these methods rely on geometric properties that can be exploited by adaptive adversaries. We introduce the Krum-Proxy attack, a selection-aware backdoor injection strategy that consistently bypasses Byzantine-robust aggregation. Rather than relying on naive scaling or constraining, our method actively optimizes malicious updates to infiltrate the dense core of the benign distribution. The proposed method constructs adversarial updates that are not only similar to benign updates but are also optimized to lie in regions of the update space that are favored during aggregation. This is achieved through a two-stage optimization procedure that separates task-specific attack objectives from geometry-aware refinement, using a nearestneighbor proxy, stochastic reference modeling, and anchorguided alignment. To maintain stealth, we introduce a projection mechanism that constrains adversarial updates within realistic norm and variance bounds. Experiments on standard federated learning benchmarks show that Krum-Proxy achieves higher attack success while preserving clean accuracy, highlighting the vulnerability of distance-based aggregation to selection-aware adversaries.

Adversarial Distance Metrics: A Threat to Fairness in Clustering-Based Decision Systems

An insider adversary manipulates the distance function to induce discriminatory clustering outcomes against demographic groups, exposing a gap in current fairness auditing practices and demonstrating the urgent need to expand the scope of the algorithmic audit to include distance-function verification.