Resilient Information Systems Security

Robustness and Transferability of Universal Attacks on Compressed Models

Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulnerable to adversarial examples-inconspicuous inputs that are specifically designed to fool these models. In particular, Universal Adversarial Perturbations (UAPs), are a powerful class of adversarial attacks which create adversarial perturbations that can generalize across a large set of inputs. In this work, we analyze the effect of various compression techniques to UAP attacks, including different forms of pruning and quantization. We test the robustness of compressed models to white-box and transfer attacks, comparing them with their […]

Object Removal Attacks on LiDAR-based 3D Object Detectors

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 elicit fake objects. In this work we demonstrate how the same physical capabilities can be used to mount a new, even more dangerous class of attacks, namely Object Removal Attacks (ORAs). ORAs aim to force 3D object detectors to fail. We leverage the default setting of LiDARs that record a single return signal per direction to perturb point clouds in the region of interest (RoI) of 3D objects. By injecting illegitimate points […]

Jacobian Regularization for Mitigating Universal Adversarial Perturbations

Authors: Kenneth Co, David Martinez Rego, Emil 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 threat as they facilitate realistic, practical, and low-cost attacks on neural networks. In this work, we derive upper bounds for the effectiveness of UAPs based on norms of data-dependent Jacobians. We empirically verify that Jacobian regularization greatly increases model robustness to UAPs by up to four times whilst maintaining clean performance. Our theoretical analysis also allows us to formulate a metric for the strength […]

Analyzing the Viability of UAV Missions Facing Cyber Attacks

With advanced video and sensing capabilities, un-occupied aerial vehicles (UAVs) are increasingly being usedfor numerous applications that involve the collaboration andautonomous operation of teams of UAVs. Yet such vehiclescan be affected by cyber attacks, impacting the viability oftheir missions. We propose a method to conduct mission via-bility analysis under cyber attacks for missions that employa team of several UAVs that share a communication network.We apply our method to a case study of a survey mission ina wildfire firefighting scenario. Within this context, we showhow our method can help quantify the expected missionperformance impact from an attack and determine if themission […]

Universal Adversarial Robustness of Texture and Shape-Biased Models

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 and shape-biased models to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN models with varying degrees of shape-based training. We find that shape-biased models do not markedly improve adversarial robustness, and we show that ensembles of texture and shape-biased models can improve universal adversarial robustness while maintaining strong performance. Citation: K. T. Co, L. Muñoz-González, L. Kanthan, B. Glocker and E. C. Lupu, “Universal Adversarial Robustness of Texture […]

Hazard Driven Threat Modelling for Cyber Physical Systems

Adversarial actors have shown their ability to infiltrate enterprise networks deployed around Cyber Physical Systems (CPSs) through social engineering, credential stealing and file-less infections. When inside, they can gain enough privileges to maliciously call legitimate APIs and apply unsafe control actions to degrade the system performance and undermine its safety. Our work lies at the intersection of security and safety, and aims to understand dependencies among security, reliability and safety in CPS/IoT. We present a methodology to perform hazard driven threat modelling and impact assessment in the context of CPSs. The process starts from the analysis of behavioural, functional and […]

Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks (CCS ’19)

Our paper on procedural noise adversarial examples has been accepted to the 26th ACM Conference on Computer and Communications Security (ACM CCS ’19). official: https://dl.acm.org/citation.cfm?id=3345660 code: https://github.com/kenny-co/procedural-advml Abstract: Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO […]

Towards More Practical Software-based Attestation

Our paper Towards More Practical Software-based Attestation has been accepted for publication by Elsevier’s Computer Networks Journal. Authors: Rodrigo Vieira Steiner, Emil Lupu Abstract: Software-based attestation promises to enable the integrity verification of untrusted devices without requiring any particular hardware. However, existing proposals rely on strong assumptions that hinder their deployment and might even weaken their security. One of such assumptions is that using the maximum known network round-trip time to define the attestation timeout allows all honest devices to reply in time. While this is normally true in controlled environments, it is generally false in real deployments and especially so in a scenario […]

A Formal Approach to Analyzing Cyber-Forensics Evidence

Erisa Karafili’s paper “A Formal Approach to Analyzing Cyber-Forensics Evidence” was accepted at the European Symposium on Research in Computer Security (ESORICS) 2018. This work is part of the AF-Cyber Project, and was a joint collaboration with King’s College London and the University of Verona. Title: A Formal Approach to Analyzing Cyber-Forensics Evidence Authors: Erisa Karafili, Matteo Cristani, Luca Viganò Abstract: The frequency and harmfulness of cyber-attacks are increasing every day, and with them also the amount of data that the cyber-forensics analysts need to collect and analyze. In this paper, we propose a formal analysis process that allows an […]

WSNs Under Attack! How Bad Is It? Evaluating Connectivity Impact Using Centrality Measures

Our paper WSNs Under Attack! How Bad Is It? Evaluating Connectivity Impact Using Centrality Measures has been presented at the Living in the Internet of Things: A PETRAS, IoTUK & IET Conference, Forum & Exhibition. Authors: Rodrigo Vieira Steiner, Martín Barrère, Emil C. Lupu Abstract: We propose a model to represent the health of WSNs that allows us to evaluate a network’s ability to execute its functions. Central to this model is how we quantify the importance of each network node. As we focus on the availability of the network data, we investigate how well different centrality measures identify the significance of each node for the […]