DETR-BAL: Decentralized mobile sensing intrusion detection via latent mining and Bayesian local optimization

Abstract

With the rapid proliferation of mobile sensing in fields such as personal health monitoring in data processing are becoming more prominent. This paper introduces a decentralized DETR framework inspired by blockchain proof-of-work consensus. The framework trains models locally on each device and evaluates the device’s reputation based on its historical performance. Only devices meeting predefined criteria are admitted to the update committee, which enhances security. This mechanism reduces reliance on centralized servers and minimizes infrastructure costs. While a supervisory operator ensures the smooth operation of the system. To further enhance trust, we propose a credibility assessment method that integrates risk metrics with data quality scores via a non-cooperative game-theoretic model. By achieving Nash equilibrium, this method not only guarantees local optimality but also prioritizes users who provide high-quality, low-risk data, thereby promoting timely committee updates to achieve global optimality. As a complement to DETR, we propose BAL-IDS, an advanced intrusion detection system (IDS) that extracts latent features using autoencoders and dynamically fine-tunes the hyperparameters of OCSVM using a Bayesian joint local agent optimization strategy. This dual approach enhances the system’s resilience to complex threats, especially those that exploit requester feedback mechanisms. Experiments show that our research is superior to traditional schemes.