5 papers
Revisiting Code Debloating with Ground Truth-based Evaluation
Muhammad Bilal, Moiz Ali, Mohit Kumar +4
Program debloating aims to remove unused code to reduce performance overhead, attack surfaces, and maintenance costs. Over time, debloating has evolved across multiple layers (cont…
AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents
Bhanu Prakash Vangala, Ali Adibifar, Ashish Gehani +1
The rise of Large Language Models (LLMs) as coding agents promises to accelerate software development, but their impact on generated code reproducibility remains largely unexplored…
GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection
Lingzhi Wang, Vinod Yegneswaran, Xinyi Shi +3
Provenance-based intrusion detection is an increasingly popular application of graphical machine learning in cybersecurity, where system activities are modeled as provenance graphs…
MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs
Prakhar Sharma, Haohuang Wen, Vinod Yegneswaran +3
The evolution toward 6G networks is being accelerated by the Open Radio Access Network (O-RAN) paradigm -- an open, interoperable architecture that enables intelligent, modular app…
Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms
Jared Claypoole, Steven Cheung, Ashish Gehani +2
We analyze two open source deep reinforcement learning agents submitted to the CAGE Challenge 2 cyber defense challenge, where each competitor submitted an agent to defend a simula…