
Discover how FAMOS framework enhances mobile payment security through federated multi-modal contrastive learning, offering robust user authentication while protecting privacy and handling background noise.

Explore the security challenges and protective strategies of Pakistani content creators, focusing on online threats, sociocultural contexts, and the lack of support systems for vulnerable creators.

Discover how BUDAlloc improves memory safety by detecting use-after-free bugs through virtual address management separation, offering better performance and reduced memory overhead compared to existing solutions.

Explore groundbreaking research on protecting digital intimate content sharing through interviews with 52 European content creators, examining threats, defense strategies, and technological solutions for preventing abuse.

Explore a novel Rowhammer exploit technique targeting nested pointer dereferences in code patterns, demonstrating enhanced data leakage capabilities and improved attack primitives in Linux kernel security.

Explore advanced vulnerability detection techniques using multi-dimensional neighbor embeddings and deep learning models to enhance software security through improved pattern recognition and data analysis.

Explore security vulnerabilities in multi-user AR applications, focusing on shared state attacks, their implications, and potential defense strategies for protecting augmented reality frameworks.

Explore how blind and low-vision users navigate digital privacy challenges, their risk perceptions, and the potential role of privacy Q&A assistants in improving their online safety and information access.

Explore a novel forensic system that enables deterministic replay and investigation of web-based attacks through JavaScript Execution Unit Partitioning, offering enhanced visibility into cyber threats.

Discover how FIRE combines multi-stage filtering and taint analysis to efficiently detect recurring vulnerabilities in open-source software, achieving 90% precision and superior performance over existing tools.

Discover how MOAT system enhances Linux kernel security by isolating BPF programs using Intel Memory Protection Keys, offering efficient protection with minimal performance impact.

Explore how Endokernel achieves secure subprocess isolation through thread-safe monitoring, efficient memory isolation, and systematic policy enforcement for enhanced application security.

Explore groundbreaking improvements to differential privacy through a novel Rank-1 Singular Multivariate Gaussian mechanism that enhances data protection while minimizing accuracy loss in high-dimensional queries.

Explore the capabilities and limitations of Large Language Models in code analysis, focusing on their effectiveness with obfuscated code and real-world applications through systematic evaluation.

Explore content moderation challenges in IPFS, examining problematic content detection, hosting patterns, and improved filtering methods for decentralized web infrastructure.

Discover how SmartCookie leverages programmable switches to block SYN flood attacks while maintaining network performance, offering innovative defense mechanisms for modern high-speed networks.

Explore how dp-promise leverages diffusion model noise to achieve differential privacy in image synthesis while maintaining high image quality and protecting sensitive training data.

Discover how SpecLFB leverages Line-Fill-Buffer to eliminate cache side-channel vulnerabilities in speculative executions, offering an efficient defense mechanism with minimal performance impact.

Discover how automated detection frameworks identify security flaws in web browser mechanisms, exploring practical approaches to validate web invariants and detect vulnerabilities across major browsers.

Explore advanced techniques for detecting ReDoS vulnerabilities in non-backtracking regex engines, featuring EvilStrGen tool and novel attack string generation methods for enhanced security testing.

Explore the challenges and solutions of binary-level thread sanitization through BINTSAN, a novel approach for detecting data races in Linux x86-64 targets without source code access.