75 citations · 125 across the 11 of their papers we have counts for
11 papers
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
Konur Tholl, François Rivest, Mariam El Mezouar +2
Autonomous Cyber Operations (ACO) are increasingly important for defending enterprise networks as cyber threats continue to evolve in sophistication. ACO applications commonly empl…
The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans
Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah
Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Perso…
AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah
Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure. When agents coordinate on tasks, sensitive data may pass thr…
Towards Production-Worthy Simulation for Autonomous Cyber Operations
Konur Tholl, Mariam El Mezouar, Adrian Taylor +1
Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emu…
A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System
Kiernan Broda-Milian, Ranwa Al-Mallah, Hanane Dagdougui
Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control methods with Deep Reinforcement Learning…
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems
Godwin Badu-Marfo, Ranwa Al Mallah, Bilal Farooq
The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were…
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
Jacob Wiebe, Ranwa Al Mallah, Li Li
Recent advancements in deep learning techniques have opened new possibilities for designing solutions for autonomous cyber defence. Teams of intelligent agents in computer network…
On the Initial Behavior Monitoring Issues in Federated Learning
Ranwa Al Mallah, Godwin Badu-Marfo, Bilal Farooq
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief. Regarding the cybersecurity of FL, some attacks ai…
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation
Ranwa Al Mallah, David Lopez, Godwin Badu Marfo +1
Federated Learning (FL) is a paradigm in Machine Learning (ML) that addresses data privacy, security, access rights and access to heterogeneous information issues by training a glo…
Prediction of Traffic Flow via Connected Vehicles
Ranwa Al Mallah, Bilal Farooq, Alejandro Quintero
We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at…
Distributed Classification of Urban Congestion Using VANET
Al Mallah Ranwa, Farooq Bilal, Quintero Alejandro
Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent…