most citedAutoPenBench: Benchmarking Generative Agents for Penetration Testing

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2024★ 3 cited

AutoPenBench: Benchmarking Generative Agents for Penetration Testing

Luca Gioacchini, Marco Mellia, Idilio Drago +3

Generative AI agents, software systems powered by Large Language Models (LLMs), are emerging as a promising approach to automate cybersecurity tasks. Among the others, penetration…

cs.LG2024

Generic Multi-modal Representation Learning for Network Traffic Analysis

Luca Gioacchini, Idilio Drago, Marco Mellia +2

Network traffic analysis is fundamental for network management, troubleshooting, and security. Tasks such as traffic classification, anomaly detection, and novelty discovery are fu…

cs.NI2024★ 2 cited

Dynamic Cluster Analysis to Detect and Track Novelty in Network Telescopes

Kai Huang, Luca Gioacchini, Marco Mellia +1

In the context of cybersecurity, tracking the activities of coordinated hosts over time is a daunting task because both participants and their behaviours evolve at a fast pace. We…

cs.AI2024★ 2 cited

AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents

Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito +4

The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmar…

cs.NE2024★ 1 cited

Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks

Giordano Paoletti, Luca Gioacchini, Marco Mellia +2

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Lo…