Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection

Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a...

NVIDIA FLARE SDK 输入验证错误漏洞

NVIDIA FLARE SDK is a federal learning application development toolkit provided by NVIDIA Corporation in the United States. The NVIDIA Flare SDK has a vulnerability related to input validation errors. This vulnerability stems from path traversal, which leads to improper input validation,...

Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT

The Internet of Battlefield Things IoBT relies on heterogeneous, bandwidth-constrained, and intermittently connected tactical networks that face rapidly evolving cyber threats. In this setting, intrusion detection cannot depend on continuous central collection of raw traffic due to disrupted link...

Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning

Federated learning FL enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as a promising solution for collaborative learning on sensitive user data, effectively addressing the longstanding privacy concerns inherent in...

FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detection

This work implements a lightweight Transformer model for IDS in the domain of Connected and Autonomous Vehicles...

A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT

The rapid growth of the Internet of Things IoT has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

AI-Based Software Vulnerability Detection: a Systematic Literature Review

Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods e.g., static analysis, rule-based matching to AI-driven approaches. This study presents a systematic review of software vulnerability detection SVD research from 2018 to...

Inclusive, Differentially Private Federated Learning for Clinical Data

Federated Learning FL offers a promising approach for training clinical AI models without centralizing sensitive patient data. However, its real-world adoption is hindered by challenges related to privacy, resource constraints, and compliance. Existing Differential Privacy DP approaches often app...

Privacy-Preserving Analytics for Smart Meter (AMI) Data: a Hybrid Approach to Comply with CPUC Privacy Regulations

Advanced Metering Infrastructure AMI data from smart electric and gas meters enables valuable insights for utilities and consumers, but also raises significant privacy concerns. In California, regulatory decisions CPUC D.11-07-056 and D.11-08-045 mandate strict privacy protections for customer...