The paper proposes a secure conformance checking method that allows a party to verify an event log against a process model while protecting the sensitive data within the log using homomorphic encrypti…

The paper introduces a certified purity architecture that strengthens governance in cognitive workflow systems by replacing insufficient runtime checks with cryptographically attested structural guara…

The paper introduces a novel pipeline integrating formal verification and process mining to systematically identify and analyze root causes of security property invalidations in complex automotive net…

This study empirically analyzed 1,000 Android apps, finding that privacy policies are often vague and frequently fail to align with the actual sensitive data logged by the applications.

The paper introduces Sieve, a system that uses a large language model (LLM) to generate executable query code from natural language security questions, significantly improving the ability to perform c…

The paper introduces EBCC, an OCI-compatible runtime architecture that manages composite confidential-computing workloads by integrating TEE-backed execution into the standard container lifecycle.

The paper introduces Heimdall, an automated pipeline that uses LLMs and formal verification to safely and automatically migrate legacy, potentially buggy eBPF programs written in C to memory-safe Rust…

The paper proposes a novel method to automatically enforce differential privacy in stream-based runtime monitoring specifications by analyzing temporal dependencies and injecting calibrated noise.

The paper presents a novel technology that uses zero-knowledge proofs to formally verify a software system's correctness against a public specification without revealing the system's internal details.

The paper introduces OpenClawBench, a large-scale dataset and framework for measuring process-side anomalies in real-world agent execution trajectories, demonstrating that task success does not guaran…

The paper characterizes logging code security issues and benchmarks LLMs, finding that while LLMs can moderately detect these issues, they struggle significantly with reliably generating correct code…

The paper introduces alignment contracts, a formal framework for specifying and enforcing behavioral constraints over observable effect traces, ensuring that powerful agentic security systems operate…

This experience report details the process and developer perceptions of integrating log-based fraud detection into an Agile workflow, providing practical best practices for embedding security analytic…

ARuleCon is an agentic framework that autonomously and accurately converts security rules across heterogeneous SIEM platforms, significantly outperforming baseline LLMs in fidelity.

SECUREVENT proposes a hybrid AI/ML security monitoring architecture that combines traditional controls with advanced behavioral analysis to secure dynamic, distributed event-based systems.

SECUREVENT proposes a hybrid AI/ML security monitoring architecture that combines traditional controls with advanced behavioral analysis to secure highly dynamic, distributed event-based systems.

The paper proposes extbackslash codeName, a behavioral firewall that uses a parameterized deterministic finite automaton (pDFA) to enforce verified benign tool-call sequences and parameter bounds for…

The paper proposes Agentic Witnessing, a TEE-enabled framework that allows external verifiers to audit the qualitative properties of private datasets by querying an LLM-based auditor without accessing…

This paper presents a novel approach for constructing information flow paths from RTL trace data for automated property generation and validation in hardware design.

The paper introduces a novel toolkit to enhance RISC-V Trusted Execution Environments (TEEs) by adding modular extensions for secure enclave update, migration, state continuity, and trusted time, ther…