160 citations · 598 across the 22 of their papers we have counts for
27 papers
ICCPS: Impact discovery using causal inference for cyber attacks in CPSs
Rajib Ranjan Maiti, Sridhar Adepu, Emil Lupu
We propose a new method to quantify the impact of cyber attacks in Cyber Physical Systems (CPSs). In particular, our method allows to identify the Design Parameter (DPs) affected d…
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Opti…
Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle Perception
Zhongyuan Hau, Soteris Demetriou, Emil C. Lupu
Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable spatial perception of their surroundings and help make driving decisions. Recent works demonstrated attac…
Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks
Kenneth T. Co, David Martinez-Rego, Zhongyuan Hau +1
Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integra…
Regularization Can Help Mitigate Poisoning Attacks... with the Right Hyperparameters
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine learning algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to degrade the algorithms' performance. We show that current a…
Real-time Detection of Practical Universal Adversarial Perturbations
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +1
Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attac…
Jacobian Regularization for Mitigating Universal Adversarial Perturbations
Kenneth T. Co, David Martinez Rego, Emil C. Lupu
Universal Adversarial Perturbations (UAPs) are input perturbations that can fool a neural network on large sets of data. They are a class of attacks that represents a significant t…
Object Removal Attacks on LiDAR-based 3D Object Detectors
Zhongyuan Hau, Kenneth T. Co, Soteris Demetriou +1
LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and their safe operations. Recent works have demonstrated that it is possible to spoof LiDAR return signals to…
Robustness and Transferability of Universal Attacks on Compressed Models
Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González +2
Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulne…
Non-equilibrium effective field theory and second sound
Michael J. Landry
We investigate the phenomenon of second sound in various states of matter from the perspective of non-equilibrium effective field theory (EFT). In particular, for each state of mat…
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1
Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Opti…
Universal Adversarial Robustness of Texture and Shape-Biased Models
Kenneth T. Co, Luis Muñoz-González, Leslie Kanthan +2
Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture a…
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
Luis Muñoz-González, Kenneth T. Co, Emil C. Lupu
Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learni…
Sensitivity of Deep Convolutional Networks to Gabor Noise
Kenneth T. Co, Luis Muñoz-González, Emil C. Lupu
Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions o…
Poisoning Attacks with Generative Adversarial Nets
Luis Muñoz-González, Bjarne Pfitzner, Matteo Russo +2
Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the…
Efficient attack countermeasure selection accounting for recovery and action costs
Jukka Soikkeli, Luis Muñoz-González, Emil C. Lupu
The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermea…
An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks
Erisa Karafili, Linna Wang, Emil C. Lupu
We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attac…
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou +1
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning a…
Mitigation of Adversarial Attacks through Embedded Feature Selection
Ziyi Bao, Luis Muñoz-González, Emil C. Lupu
Machine learning has become one of the main components for task automation in many application domains. Despite the advancements and impressive achievements of machine learning, it…
Extracting Randomness From The Trend of IPI for Cryptographic Operators in Implantable Medical Devices
Hassan Chizari, Emil Lupu
Achieving secure communication between an Implantable Medical Device (IMD) inside the body and a gateway outside the body has showed its criticality with recent reports of hackings…
Label Sanitization against Label Flipping Poisoning Attacks
Andrea Paudice, Luis Muñoz-González, Emil C. Lupu
Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in…
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
Andrea Paudice, Luis Muñoz-González, Andras Gyorgy +1
Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among other…
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis +4
A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of d…
Argumentation-based Security for Social Good
Erisa Karafili, Antonis C. Kakas, Nikolaos I. Spanoudakis +1
The increase of connectivity and the impact it has in every day life is raising new and existing security problems that are becoming important for social good. We introduce two par…