activity
20162026
most citedLOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

6 citations · 19 across the 26 of their papers we have counts for

collaborators

30 papers

cs.CR2026

Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents

Sabrina Saika, Yinuo Du, Aritran Piplai

Cyber attack agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the environments in which they were de…

cs.CR2026

RuleAutoPilot: Synthesizing Deployable Suricata Rules from Network Traffic

Mughees Ur Rehman, Aritran Piplai, Murat Kantarcioglu

Rule-based Intrusion Detection Systems (IDS) such as Suricata are central to network security, yet crafting effective detection rules demands deep expert knowledge and cannot keep…

cs.CL2026

Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA

Shaswata Mitra, Subash Neupane, Trisha Chakraborty +4

Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation,…

cs.LG2026

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification

Ivan Alejandro Montoya Sanchez, Anantaa Kotal, Aritran Piplai

Cybersecurity systems must adapt rapidly to emerging threats. However, labeled data for new threat categories is unavailable when those threats first appear. Generalized zero-shot…

cs.CR2026

McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware

Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi +5

Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware natur…

cs.CR2026

Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents

Ondřej Lukáš, Jihoon Shin, Emilia Rivas +6

Autonomous offensive agents often fail to transfer beyond the networks on which they are trained. We isolate a minimal but fundamental shift -- unseen host/subnet IP reassignment i…