Federated Learning Architecture: Data Privacy and System Security Approaches

This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning FL systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information...

Securing Autonomous Vehicle Systems Via Twin-Aware Federated Reinforcement Learning

Federated reinforcement learning FRL is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a significant threat...

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

Large Language Models LLMs and generative AI GenAI systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks...

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

The rapid proliferation of Internet of things IoT devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems IDS. However, centralized learning approaches often suffer from severe performance degradation due to...

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

Intrusion Detection Systems IDSs are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things IoT, enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behavio...

An AI-Based Solution for Secure Service Provisioning in IoT

As the Internet of Things IoT continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this evolving landscape, securing service provisioning is crucial to ensure the proper functioning, security, and reliability...

Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design

The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device machine learning applications. While federated learning FL mitigates privacy concerns by exchanging model parameters instead of raw data, we identi...

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Prior research suggests that differential privacy DP inherently enhances the robustness of federated learning FL against backdoor attacks. In this paper, we challenge this assumption. Through an empirical analysis of two baseline attack strategies, we uncover a fundamental tension in DP-FL: while...

SwarmSense-DNN: A Trustworthy and Decentralized Neural Framework for Proactive Anomaly Defense in Consumer IoT

The rapid growth of consumer IoT devices has introduced unprecedented challenges in trustworthy anomaly detection against AI-enabled cyber threats, requiring real-time, privacy-preserving, and scalable defense mechanisms. Traditional centralized strategies face critical limitations, including...

Model Poisoning against Federated Model Adaptation with Chain of Bit-Flips

Federated Learning FL allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third party potentially poison the training...

Cognitive Threat Intelligence and Explainable Federated Security Analytics for Distributed Infrastructure Systems

The increasing adoption of distributed infrastructure systems, cloud computing, Internet of Things IoT technologies, and edge-based architectures has significantly expanded the cybersecurity attack surface and introduced increasingly sophisticated cyber threats. Conventional centralized intrusion...

SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface:...

AI Security Research Should Better Incentivize Defense Research

This work examines an imbalance in artificial intelligence AI security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech...

XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

An Intrusion Detection System IDS is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering ...

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...

Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications

Artificial Intelligence AI is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis,...

AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics

Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...

A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

AI-powered edge computing security is moving Intelligent Transportation Systems ITS from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT,...

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,...

Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection

The global financial ecosystem confronts a critical asymmetry: while fraud syndicates operate as borderless, distributed networks, banking institutions remain constrained by regulatory data silos, limiting visibility into cross-institutional threat patterns under strict privacy laws such as GDPR...