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Explore automated DRAM traffic analysis revealing critical gaps in DDR5 Rowhammer defenses and memory controller behaviors using the McSee platform.
Explore systematic analysis of secure randomized cache designs and their defense mechanisms against side-channel attacks in modern computer systems.
Discover DHAttack, a novel label-only membership inference attack that achieves superior performance with minimal queries, overcoming sample and direction diversity challenges.
Uncover how "Hi Mum and Dad" SMS scams work through empirical research analyzing 711 scammer interactions, revealing deception tactics and mitigation strategies.
Uncover novel CPU vulnerabilities through branch prediction analysis, revealing three new attack primitives including Spectre variants and BiasScope for cross-privilege exploitation.
Discover how researchers successfully executed the first Rowhammer attack on Intel DDR4 ECC servers, bypassing memory protections to flip bits and compromise RSA keys without crashes.
Discover how to achieve both differential privacy and Byzantine robustness in federated learning using client momentum to expose malicious attacks while preserving data privacy.
Uncover how personal impersonation attacks exploit social media trust to redirect digital payments, revealing attacker tactics and proposing platform security enhancements.
Discover how Encarsia framework automatically injects bugs into RISC-V CPU designs to evaluate hardware fuzzer effectiveness and reveal limitations in current testing approaches.
Discover BLuEMan, a simulation-based fuzzing framework for testing Bluetooth Low Energy protocol stacks in RTOS environments, achieving 18-162x faster testing speeds and uncovering new CVEs.
Explore Thunderdome, a revolutionary timelock-free payment channel network that uses wardens and game theory to enable secure multi-hop blockchain transactions.
Discover how to detect fake base stations and multi-step attacks in cellular networks using machine learning techniques with 96% accuracy and real-time protection capabilities.
Explore a unified taxonomy and formal definitions for data reconstruction attacks against ML models, featuring new evaluation metrics and LLM-based visual assessment frameworks.
Discover how acoustic waves can manipulate system time through RTC vulnerabilities, enabling forward/backward timing drift attacks with up to 93% accuracy on real-time systems.
Uncover vulnerabilities in ML/AI conference reviewer assignment systems and how collusion rings exploit text-matching algorithms to manipulate peer review processes.
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