anomaly-flow

Anomaly-Flow Framework for implementing Intrusion Detection Systems NIDS aimed at identifying anomalies in network flows using Machine Learning models. Reference paper: Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection Create the...

FML-Network

FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning Paper: FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning Dataset: FLNET2023 Introduction FLNET2023 is a state-of-the-art benchmark dataset for intrusion detection systems, specifically fo...

A Novel Federated Learning Framework for Defending against Gradient Inversion Attacks

Federated Learning FL offers a paradigm for collaborative machine learning model training among clients, preserving data privacy by avoiding direct data sharing. However, the gradient information exchanged in FL has been shown to be vulnerable to Gradient Inversion Attacks GIA, which can...

pvbrf-id

PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks Simulation code, raw results and figure scripts for the paper Mustafa S. Aljumaily, Ali Ataeemh Allami, Mustafa N. Mnati, Nawar S. Alseelawi, Shahab Abdulla, "PVBRF-ID: Private,...

VeilFL: Two-Stage Accountability for Anonymous Federated Learning

Anonymous credentials can hide which registered client participates in federated learning, but their unlinkability also makes repeated misconduct difficult to associate. We present VeilFL, a two-aggregator protocol that adds staged accountability to PINE USENIX Security '24. Each submission binds...

Characterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-Of-Experts LLMs

As Mixture-of-Experts MoE large language models are increasingly deployed in distributed environments, from federated learning to edge-cloud systems, their architecture harbors an underexplored landscape of security vulnerabilities. Moving beyond textual analysis, we uncover structural...

Trustworthy Distributed Machine Learning in Cloud-Edge-IoT Systems: A Heterogeneity-Aware Survey of Security, Privacy, and Robustness

Distributed machine learning DML, including conventional distributed training and federated learning FL, enables collaborative model development across cloud-edge-IoT systems without fully centralizing raw data. However, distributing data, computation, updates, and orchestration increases the...

On the Vulnerabilities and Data-Free Removal of Watermarks in Batch Normalization Layers

Batch Normalization BN layers have become a common choice for embedding white-box watermarks in Federated Learning FL, providing a simple and effective mechanism for model ownership verification and participant identification. However, previous works have focused on robustness against generic mod...

StratFL: Countering Covert Model Poisoning Attacks with Strategic Federated Learning

Advanced persistent threats APTs are a major concern in federated learning FL deployments in critical sectors such as healthcare, and finance. Traditional defense mechanisms fail to contain APTs due to their intermittent attacking behavior. This paper addresses a specific type of APT called cover...

Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection

Network Intrusion Detection Systems NIDS based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated...

Toward Realistic Privacy Risk in FL-Enabled OT Intrusion Detection Systems

In Operational Technology OT environments, Federated Learning FL is increasingly adopted to train Intrusion Detection Systems IDSs without sharing raw operational data across facilities. However, model updates may still leak sensitive information about industrial processes, creating privacy risks...

Enhanced Detective: A Dual-Stage Proactive Defense against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning

With recent developments in Artificial Intelligence AI, the adoption of distributed learning paradigms such as Federated Learning FL has emerged. In simple terms, FL enables multiple entities to collaboratively train AI models without sharing local data. The FL paradigm is particularly valuable i...

PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks

Federated learning allows dispersed oilfield sites to train a shared intrusion detector without pooling network traffic, yet its aggregation step forces a choice between privacy and robustness. Byzantine-robust rules must read each update in the clear, whereas secure aggregation hides updates fro...

Receiver-Domain Behavioral Probing for Backdoor-Resilient Federated GPS Spoofing Detection in UAV Networks

Federated learning lets a UAV fleet train a shared GPS spoofing detector without raw receiver data leaving any aircraft, and several recent UAV-FL designs weight each client by the validation accuracy it reports about itself. We show this self-report is an exploitable attack lever: two compromise...

Compression Footprints As Security Signals for Model-Poisoning Defense in Federated Learning

Lossy compression is widely used in Federated Learning FL but is generally treated as an error source, while conventional poisoning defenses inspect update geometry. In this work, we instead treat the compressor's response as a security signal: the input-dependent distortion and payload behavior...

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things IoT devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentiall...

Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure

Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetr...

DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning

Decentralized Federated Learning DFL eliminates the central aggregation server, reducing the single point of observation that traditional defenses against attacks rely on. As a result, peer-to-peer networks become exposed to malicious updates containing backdoors or semantic poisoning, since such...

Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications

Blockchain and artificial intelligence AI are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across distributed systems. This paper presents a meta-synthesis that draws together four constituent studies covering adversaria...

Reliable Federated TinyML Deployment for IoT Security

The growing deployment of Internet of Things IoT devices has increased the need for privacy-preserving intrusion detection systems that operate directly on resource-constrained hardware. Federated Learning enables collaborative model training without sharing raw data, but conventional federated...