Emil Lupu

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

Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou, and Emil C. Lupu. 2019. Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS ’19). Association for Computing Machinery, New York, NY, USA, 275–289. DOI:https://doi.org/10.1145/3319535.3345660

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 v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.

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Fulvio Valenza

Fulvio was a Visiting Researcher in the group working on various aspects relating to hybrid-threats and formal analysis applied to network policies. He is with the department of Control and Computer Engineering at the Politecnico di Torino and continues to work in cyber-security within the context of network and systems management.

MUSKETEER: Machine learning to augment shared knowledge in federated privacy-preserving scenarios

The massive increase in data collected and stored worldwide calls for new ways to preserve privacy while still allowing data sharing among multiple data owners. Today, the lack of trusted and secure environments for data sharing inhibits data economy while legality, privacy, trustworthiness, data value and confidentiality hamper the free flow of data. By the end of the project, MUSKETEER aims to create a validated, federated, privacy-preserving machine learning platform tested on industrial data that is inter-operable, scalable and efficient enough to be deployed in real use cases. MUSKETEER aims to alleviate data sharing barriers by providing secure, scalable and privacy-preserving analytics over decentralized datasets using machine learning. Data can continue to be stored in different locations with different privacy constraints, but shared securely. The MUSKETEER cross-domain platform will validate progress in the industrial scenarios of smart manufacturing and health. MUSKETEER strives to (1) create machine learning models over a variety of privacy-preserving scenarios, (2) ensure security and robustness against external and internal threats, (3) provide a standardized and extendable architecture, (4) demonstrate and validate in two different industrial scenarios and (5) enhance data economy by boosting sharing across domains. The MUSKETEER impact crosses industrial, scientific, economic and strategic domains. Real-world industry requirements and outcomes are validated in an operational setting. Federated machine learning approaches for data sharing are innovated. Data economy is fostered by creating a rewarding model capable of fairly monetizing datasets according to the real data value. Finally, Europe is positioned as a leader in innovative data sharing technologies.

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H2020

Towards More Practical Software-based Attestation

Rodrigo Vieira Steiner, EmilLupu, Towards more practical software-based attestation, J. Computer Networks, v. 149, pp 43-55, Elsevier, 2019.

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 like the Internet of Things where numerous devices communicate over an intrinsically unreliable wireless medium. Moreover, a larger timeout demands more computations, consuming extra time and energy and restraining the untrusted device from performing its main tasks. In this paper, we review this fundamental and yet overlooked assumption and propose a novel stochastic approach that significantly improves the overall attestation performance. Our experimental evaluation with IoT devices communicating over real-world uncontrolled Wi-Fi networks demonstrates the practicality and superior performance of our approach that in comparison with the current state of the art solution reduces the total attestation time and energy consumption around seven times for honest devices and two times for malicious ones, while improving the detection rate of honest devices (8% higher TPR) without compromising security (0% FPR).

Paper

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 analyst to filter the enormous amount of evidence collected and either identify crucial information about the attack (e.g., when it occurred, its culprit, its target) or, at the very least, perform a pre-analysis to reduce the complexity of the problem in order to then draw conclusions more swiftly and efficiently. We introduce the Evidence Logic EL for representing simple and derived pieces of evidence from different sources. We propose a procedure, based on monotonic reasoning, that rewrites the pieces of evidence with the use of tableau rules, based on relations of trust between sources and the reasoning behind the derived evidence, and yields a consistent set of pieces of evidence. As proof of concept, we apply our analysis process to a concrete cyber-forensics case study.

 

You can find the paper here.

This work was funded from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 746667.

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 network connectivity. In this process, we propose a new metric named current-flow sink betweenness. Through a number of experiments , we demonstrate that while no metric is invariably better in identifying sensors’ connectivity relevance, the proposed current-flow sink betweenness outperforms existing metrics in the vast majority of cases.

Download a copy here.

Kenneth Co

Kenny joined the group as a PhD student in April 2018. He received an MSc in Machine Learning from Imperial College London and an MA in Mathematics from Johns Hopkins University.

His general interests are in machine learning, cryptography, and mathematics. His current research is on the security of machine learning algorithms, primarily adversarial machine learning. He is also interested in health or lifestyle optimization, and is very much into enjoying good food.

Find him on LinkedIn.

Ensuring the resilience of WSN to Malicious Data Injections through Measurements Inspection

Vitorio Paolo Illiano, PhD Thesis

Malicious data injections pose a severe threat to the systems based on Wireless Sensor Networks (WSNs) since they give the attacker control over the measurements, and on the system’s status and response in turn. Malicious measurements are particularly threatening when used to spoof or mask events of interest, thus eliciting or preventing desirable responses. Spoofing and masking attacks are particularly difficult to detect since they depict plausible behaviours, especially if multiple sensors have been compromised and collude to inject a coherent set of malicious measurements. Previous work has tackled the problem through measurements inspection, which analyses the inter-measurements correlations induced by the physical phenomena. However, these techniques consider simplistic attacks and are not robust to collusion. Moreover, they assume highly predictable patterns in the measurements distribution, which are invalidated by the unpredictability of events. We design a set of techniques that effectively detect malicious data injections in the presence of sophisticated collusion strategies, when one or more events manifest. Moreover, we build a methodology to characterise the likely compromised sensors. We also design diagnosis criteria that allow us to distinguish anomalies arising from malicious interference and faults. In contrast with previous work, we test the robustness of our methodology with automated and sophisticated attacks, where the attacker aims to evade detection. We conclude that our approach outperforms state-of-the-a
rt approaches. Moreover, we estimate quantitatively the WSN degree of resilience and provide a methodology to give a WSN owner an assured degree of resilience by automatically designing the WSN deployment. To deal also with the extreme scenario where the attacker has compromised most of the WSN, we propose a combination with software attestation techniques, which are more reliable when malicious data is originated by a compromised software, but also more expensive, and achieve an excellent trade-off between cost and resilience.

Download Thesis from here. 

Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks

Vittorio P. Illiano, Andrea Paudice, Luis Muñoz-González, and Emil C. Lupu. 2018. Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks. ACM Trans. Sen. Netw. 14, 1, Article 5 (February 2018), 35 pages. DOI: https://doi.org/10.1145/3176621

Abstract: Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack’s chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system’s resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement.

AF-Cyber: Logic-based Attribution and Forensics in Cyber Security

Connected devices will continue to grow in volume and variety. The increase of connectivity brings a drastic impact on the increase of cyber attacks. Protecting measurements are not enough, while finding who did the attack is a crucial for preventing the escalation of cyber attacks. The impact of forensics in cyber security is becoming essential for the reduction and mitigation of attacks. Forensics and attribution forensics come along with their own challenges, like the difficulties on collecting suitable evidence, and the vastness of anti-forensics tools used by the attackers to cover their traces.

The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.

AF-Cyber received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746667.

AF-Cyber: Logic-based Attribution and Forensics in Cyber Security

Dr Karafili is officially a Marie Curie Fellow at the Department of Computing, Imperial College. She will work on the project “AF-Cyber: Logic-based Attribution and Forensics in Cyber Security“.  The project was granted by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 746667.

 

AF-Cyber: Logic-based Attribution and Forensics in Cyber Security

The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.

Improving Data Sharing in Data Rich Environments

The paper “Improving Data Sharing in Data Rich Environments” was accepted at the IEEE Big Data International Workshop on Policy-based Autonomic Data Governance (PADG), part of the 15th IEEE International Conference on Big Data (Big Data 2017), December 11-14, 2017, Boston, MA, USA. This work was done in collaboration with our partners (BAE Systems, IBM UK and IBM US) from the DAIS International Technology Alliance (ITA). The paper can be found here.

Authors: Erisa Karafili, Emil C. Lupu, Alan Cullen, Bill Williams, Saritha Arunkumar, Seraphin Calo

Abstract: The increasing use of big data comes along with the problem of ensuring correct and secure data access. There is a need to maximise the data dissemination whilst controlling their access. Depending on the type of users different qualities and parts of data are shared. We introduce an alteration mechanism, more precisely a restriction one, based on a policy analysis language. The alteration reflects the level of trust and relations the users have, and are represented as policies inside the data sharing agreements. These agreements are attached to the data and are enforced every time the data are accessed, used or shared. We show the use of our alteration mechanism with a military use case, where different parties are involved during the missions, and they have different relations of trust and partnership.

The work was supported by EPSRC Project CIPART grant no. EP/L022729/1 and DAIS ITA (Sponsored by U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001).

 

Tracking the Bad Guys: An Efficient Forensic Methodology To Trace Multi-step Attacks Using Core Attack Graphs

Martín Barrère, Rodrigo Vieira Steiner, Rabih Mohsen, Emil C. Lupu, Tracking the Bad Guys: An Efficient Forensic Methodology To Trace Multi-step Attacks Using Core Attack Graphs, 13th IEEE/IFIP International Conference on Network and Service Management (CNSM’17), November 2017, in Tokyo, Japan.

In this paper, we describe an efficient methodology to guide investigators during network forensic analysis. To this end, we introduce the concept of core attack graph, a compact representation of the main routes an attacker can take towards specific network targets. Such compactness allows forensic investigators to focus their efforts on critical nodes that are more likely to be part of attack paths, thus reducing the overall number of nodes (devices, network privileges) that need to be examined. Nevertheless, core graphs also allow investigators to hierarchically explore the graph in order to retrieve different levels of summarised information. We have evaluated our approach over different network topologies varying parameters such as network size, density, and forensic evaluation threshold. Our results demonstrate that we can achieve the same level of accuracy provided by standard logical attack graphs while significantly reducing the exploration rate of the network.

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Presentation

Naggen: a Network Attack Graph GENeration Tool

Martin Barrere and Emil C. Lupu, Naggen: a Network Attack Graph GENeration Tool, IEEE Conference on Communications and Network Security (CNS’17), October 2017, in Las Vegas, USA.

Attack graphs constitute a powerful security tool aimed at modelling the many ways in which an attacker may compromise different assets in a network. Despite their usefulness in several security-related activities (e.g. hardening, monitoring, forensics), the complexity of these graphs can massively grow as the network becomes denser and larger, thus defying their practical usability. In this presentation, we first describe some of the problems that currently challenge the practical use of attack graphs. We then explain our approach based on core attack graphs, a novel perspective to address attack graph complexity. Finally, we present Naggen, a tool for generating, visualising and exploring core attack graphs. We use Naggen to show the advantages of our approach on different security applications.

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Argumentation-based Security for Social Good

The paper “Argumentation-based Security for Social Good” presented at the AAAI Spring Symposia 2017 is now available at the AAAI Technical Report.

Title: Argumentation-Based Security for Social Good

Authors: Erisa Karafili, Antonis C. Kakas, Nikolaos I. Spanoudakis, Emil C. Lupu

Abstract: The increase of connectivity and the impact it has in ever day life is raising new and existing security problems that are becoming important for social good. We introduce two particular problems: cyber attack attribution and regulatory data sharing. For both problems, decisions about which rules to apply, should be taken under incomplete and context dependent information. The solution we propose is based on argumentation reasoning, that is a well suited technique for implementing decision making mechanisms under conflicting and incomplete information. Our proposal permits us to identify the attacker of a cyber attack and decide the regulation rule that should be used while using and sharing data. We illustrate our solution through concrete examples.

The paper can be found in the following link: https://aaai.org/ocs/index.php/FSS/FSS17/paper/view/15928/15306

A video of the presentation can be found in the workshop page AI for Social Good and also in following link: https://youtu.be/wYg8jaHPbyw?t=33m33s

An argumentation reasoning approach for data processing

The paper “An argumentation reasoning approach for data processing” is now published in the Elsevier Journal Computers in Industry.

Title: An argumentation reasoning approach for data processing

Authors: Erisa Karafili, Konstantina Spanaki, Emil C. Lupu

Abstract: Data-intensive environments enable us to capture information and knowledge about the physical surroundings, to optimise our resources, enjoy personalised services and gain unprecedented insights into our lives. However, to obtain these endeavours extracted from the data, this data should be generated, collected and the insight should be exploited. Following an argumentation reasoning approach for data processing and building on the theoretical background of data management, we highlight the importance of data sharing agreements (DSAs) and quality attributes for the proposed data processing mechanism. The proposed approach is taking into account the DSAs and usage policies as well as the quality attributes of the data, which were previously neglected compared to existing methods in the data processing and management field. Previous research provided techniques towards this direction; however, a more intensive research approach for processing techniques should be introduced for the future to enhance the value creation from the data and new strategies should be formed around this data generated daily from various devices and sources.

This work was supported by FP7 EU-funded project Coco Cloud grant no.: 610853, and EPSRC Project CIPART grant no. EP/L022729/1.

The paper can be found in the following link as Open Access: http://www.sciencedirect.com/science/article/pii/S016636151730338X