Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection
The research presents a three-stage training model for intrusion detection in edge computing devices. The model uses…
opML: Optimistic Machine Learning on Blockchain
The article discusses the integration of machine learning with blockchain technology, specifically through opML (Optimistic Machine Learning…
Bitcoin Inscriptions: Foundations and Beyond
This report delves into the concept of Bitcoin inscriptions and their technological foundations. It provides a detailed…
Ambush from All Sides: Understanding Security Threats in Open-Source Software CI/CD Pipelines
This paper presents a comprehensive analysis of security threats in Continuous Integration and Continuous Deployment (CI/CD) pipelines,…
Beyond Control: Exploring Novel File System Objects for Data-Only Attacks on Linux Systems
The article discusses the rise of non-control data attacks, especially in the domain of OS kernel exploits,…
Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget
This research paper addresses the issue of maintaining privacy budget in differentially-private (DP) systems. Authors propose ElephantDP,…
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures
The article focuses on distillation-based federated learning, a collaborative learning approach that reduces the risk of privacy…
QTFlow: Quantitative Timing-Sensitive Information Flow for Security-Aware Hardware Design on RTL
QTFlow is a new framework introduced in this research to enhance security in hardware design. Current Electronic…
GuardFS: a File System for Integrated Detection and Mitigation of Linux-based Ransomware
The paper discusses the ongoing threat of ransomware, especially in Linux-based systems. It introduces GuardFS, a file…
Benchmarking Private Population Data Release Mechanisms: Synthetic Data vs. TopDown
This study compares the TopDown algorithm and private synthetic data generation methods in terms of differential privacy….