On Automating Security Policies with Contemporary LLMs

The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models LLMs for automating attack mitigation policy...

FedShield-LLM: a Secure and Scalable Federated Fine-Tuned Large Language Model

Federated Learning FL offers a decentralized framework for training and fine-tuning Large Language Models LLMs by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associate...

A Symmetric LWE-Based Multi-Recipient Cryptosystem

This article describes a post-quantum multirecipient symmetric cryptosystem whose security is based on the hardness of the LWE problem. In this scheme a single sender encrypts multiple messages for multiple recipients generating a single ciphertext which is broadcast to the recipients. Each...

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning FL solutions in the vehicular intrusion detectio...

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...

PT-2025-25491 · Crates.Io · Anon-Vec

The following functions in the anon-vec crate are unsound due to insufficient checks on their arguments:: - AnonVec::get ref - AnonVec::get mut - AnonVec::remove get The crate was built as a learning project and is not being maintained...

Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning

Federated learning FL allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning PEFT of large-scale...

Towards Trustworthy Federated Learning with Untrusted Participants

Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assumption of a trusted central server. This paper shows that a significantly weaker assumption suffices: each pair of...

QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality

This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices. Our approach trains specialized local models for different image quality levels and aggregates their features...

A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges

IDS aims to protect computer networks from security threats by detecting, notifying, and taking appropriate action to prevent illegal access and protect confidential information. As the globe becomes increasingly dependent on technology and automated processes, ensuring secured systems,...

Poster: FedBlockParadox -- a Framework for Simulating and Securing Decentralized Federated Learning

A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluat...

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation

Federated Learning FL enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, wh...

CSVAR: Enhancing Visual Privacy in Federated Learning Via Adaptive Shuffling against Overfitting

Although federated learning preserves training data within local privacy domains, the aggregated model parameters may still reveal private characteristics. This vulnerability stems from clients' limited training data, which predisposes models to overfitting. Such overfitting enables models to...

Fingerprinting Deep Learning Models Via Network Traffic Patterns in Federated Learning

Federated Learning FL is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in...

A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT

The widespread adoption of the Internet of Things IoT has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building...

A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems

Modern transportation systems rely on cyber-physical systems CPS, where cyber systems interact seamlessly with physical systems like transportation-related sensors and actuators to enhance safety, mobility, and energy efficiency. However, growing automation and connectivity increase exposure to...

Robust and Verifiable MPC with Applications to Linear Machine Learning Inference

In this work, we present an efficient secure multi-party computation MPC protocol that provides strong security guarantees in settings with dishonest majority of participants who may behave arbitrarily. Unlike the popular MPC implementation known as SPDZ Crypto '12, which only ensures security wi...

Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges

Large Language Models LLMs still struggle with the structured reasoning and tool-assisted computation needed for problem solving in cybersecurity applications. In this work, we introduce "random-crypto", a cryptographic Capture-the-Flag CTF challenge generator framework that we use to fine-tune a...

Shadow Defense against Gradient Inversion Attack in Federated Learning

Federated learning FL has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is...

CHIP: Chameleon Hash-Based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control

The pervasion of large-scale Deep Neural Networks DNNs and their enormous training costs make their intellectual property IP protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against...