Constructing and Benchmarking: A Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework

Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models LLMs to craft highly deceptive content. This study presents a comprehensive email dataset containing phishing, spam, and legitimate messages, explicitly distinguishing betwee...

StealthCup: Realistic, Multi-Stage, Evasion-Focused CTF for Benchmarking IDS

Intrusion Detection Systems IDS are critical to defending enterprise and industrial control environments, yet evaluating their effectiveness under realistic conditions remains an open challenge. Existing benchmarks rely on synthetic datasets e.g., NSL-KDD, CICIDS2017 or scripted replay frameworks...

Benchmarking NVIDIA RTX Pro 6000 Blackwell on Akamai Cloud

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ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research

The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial...

Microsoft raises the bar: A smarter way to measure AI for cybersecurity

ExCyTIn-Bench is Microsoft’s newest open-source benchmarking tool designed to evaluate how well AI systems perform real-world cybersecurity investigations.1 It helps business leaders assess language models by simulating realistic cyberthreat scenarios and providing clear, actionable insights into...

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Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System

Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models LLMs have the potential to automate and scale Security...

End-To-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems

Intelligent Transportation Systems ITS have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time...

Maturing the cyber threat intelligence program

The Cyber Threat Intelligence Capability Maturity Model CTI-CMM helps organizations assess and improve their threat intelligence programs by outlining 11 key areas and specific missions where CTI can support decision-making. The model describes four levels of maturity, guiding teams from basic, a...

All You Need Is a Fuzzing Brain: an LLM-Powered System for Automated Vulnerability Detection and Patching

Our team, All You Need Is A Fuzzing Brain, was one of seven finalists in DARPA's Artificial Intelligence Cyber Challenge AIxCC, placing fourth in the final round. During the competition, we developed a Cyber Reasoning System CRS that autonomously discovered 28 security vulnerabilities - including...

Benchmarking VPUs and GPUs for Media Workloads

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HEIR: a Universal Compiler for Homomorphic Encryption

This work presents Homomorphic Encryption Intermediate Representation HEIR, a unified approach to building homomorphic encryption HE compilers. HEIR aims to support all mainstream techniques in homomorphic encryption, integrate with all major software libraries and hardware accelerators, and...

Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems

Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols. This connectivity, while powerful, introduces novel security risks. One such risk is a cascading risk: a breach in one agent c...

Transparency on Microsoft Defender for Office 365 email security effectiveness

In today’s world, cyberattackers are relentless. They are often well-resourced, highly sophisticated, and constantly innovating, which means the effectiveness of cybersecurity solutions must be continuously evaluated, not assumed. Yet, despite the critical role email security plays in protecting...

MH-FSF: a Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation

Feature selection is vital for building effective predictive models, as it reduces dimensionality and emphasizes key features. However, current research often suffers from limited benchmarking and reliance on proprietary datasets. This severely hinders reproducibility and can negatively impact...

LDP$^3$: an Extensible and Multi-Threaded Toolkit for Local Differential Privacy Protocols and Post-Processing Methods

Local differential privacy LDP has become a prominent notion for privacy-preserving data collection. While numerous LDP protocols and post-processing PP methods have been developed, selecting an optimal combination under different privacy budgets and datasets remains a challenge. Moreover, the la...

LIFT: Automating Symbolic Execution Optimization with Large Language Models for AI Networks

Dynamic Symbolic Execution DSE is a key technique in program analysis, widely used in software testing, vulnerability discovery, and formal verification. In distributed AI systems, DSE plays a crucial role in identifying hard-to-detect bugs, especially those arising from complex network...

FIDESlib: a Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs

Word-wise Fully Homomorphic Encryption FHE schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving approximate computing; an especially desirable feature in Machine-Learning-as-a-Service MLaaS cloud-computing paradigms...

Can Large Language Models Automate the Refinement of Cellular Network Specifications?

Cellular networks serve billions of users globally, yet concerns about reliability and security persist due to weaknesses in 3GPP standards. However, traditional analysis methods, including manual inspection and automated tools, struggle with increasingly expanding cellular network specifications...