225 results found
Steiner RV, Lupu E, 2019, Towards more practical software-based attestation, COMPUTER NETWORKS, Vol: 149, Pages: 43-55, ISSN: 1389-1286
Collinge G, Lupu E, Munoz Gonzalez L, Defending against Poisoning Attacks in Online Learning Settings, European Symposium on Artificial Neural Networks
Paudice A, Muñoz-González L, Lupu EC, 2019, Label Sanitization Against Label Flipping Poisoning Attacks, Pages: 5-15, ISSN: 0302-9743
© 2019, Springer Nature Switzerland AG. 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 the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points. Even if the capabilities of the attacker are constrained, these attacks have been shown to be effective to significantly degrade the performance of the system. In this paper we propose an efficient algorithm to perform optimal label flipping poisoning attacks and a mechanism to detect and relabel suspicious data points, mitigating the effect of such poisoning attacks.
Karafili E, Spanaki K, Lupu EC, 2019, Access control and quality attributes of open data: Applications and techniques, Pages: 603-614, ISSN: 1865-1348
© Springer Nature Switzerland AG 2019. Open Datasets provide one of the most popular ways to acquire insight and information about individuals, organizations and multiple streams of knowledge. Exploring Open Datasets by applying comprehensive and rigorous techniques for data processing can provide the ground for innovation and value for everyone if the data are handled in a legal and controlled way. In our study, we propose an argumentation and abductive reasoning approach for data processing which is based on the data quality background. Explicitly, we draw on the literature of data management and quality for the attributes of the data, and we extend this background through the development of our techniques. Our aim is to provide herein a brief overview of the data quality aspects, as well as indicative applications and examples of our approach. Our overall objective is to bring serious intent and propose a structured way for access control and processing of open data with a focus on the data quality aspects.
Muñoz-González L, Lupu EC, 2019, The security of machine learning systems, Intelligent Systems Reference Library, Pages: 47-79
© Springer Nature Switzerland AG 2019. Machine learning lies at the core of many modern applications, extracting valuable information from data acquired from numerous sources. It has produced a disruptive change in society, providing new functionality, improved quality of life for users, e.g., through personalization, optimized use of resources, and the automation of many processes. However, machine learning systems can themselves be the targets of attackers, who might gain a significant advantage by exploiting the vulnerabilities of learning algorithms. Such attacks have already been reported in the wild in different application domains. This chapter describes the mechanisms that allow attackers to compromise machine learning systems by injecting malicious data or exploiting the algorithms’ weaknesses and blind spots. Furthermore, mechanisms that can help mitigate the effect of such attacks are also explained, along with the challenges of designing more secure machine learning systems.
, 2019, AI in Cybersecurity, Publisher: Springer International Publishing, ISBN: 9783319988412
Spanaki K, Gürgüç Z, Mulligan C, et al., 2018, Organizational cloud security and control: a proactive approach, Information Technology and People, ISSN: 0959-3845
© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to unfold the perceptions around additional security in cloud environments by highlighting the importance of controlling mechanisms as an approach to the ethical use of the systems. The study focuses on the effects of the controlling mechanisms in maintaining an overall secure position for the cloud and the mediating role of the ethical behavior in this relationship. Design/methodology/approach: A case study was conducted, examining the adoption of managed cloud security services as a means of control, as well as a large-scale survey with the views of IT decision makers about the effects of such adoption to the overall cloud security. Findings: The findings indicate that there is indeed a positive relationship between the adoption of controlling mechanisms and the maintenance of overall cloud security, which increases when the users follow an ethical behavior in the use of the cloud. A framework based on the findings is built suggesting a research agenda for the future and a conceptualization of the field. Research limitations/implications: One of the major limitations of the study is the fact that the data collection was based on the perceptions of IT decision makers from a cross-section of industries; however the proposed framework should also be examined in industry-specific context. Although the firm size was indicated as a high influencing factor, it was not considered for this study, as the data collection targeted a range of organizations from various sizes. Originality/value: This study extends the research of IS security behavior based on the notion that individuals (clients and providers of cloud infrastructure) are protecting something separate from themselves, in a cloud-based environment, sharing responsibility and trust with their peers. The organization in this context is focusing on managed security solutions as a proactive measurement to preserve cloud security in cloud e
© 2018 Association for Computing Machinery. Access control for information has primarily focused on access statically granted to subjects by administrators usually in the context of a specific system. Even if mechanisms are available for access revocation, revocations must still be executed manually by an administrator. However, as physical devices become increasingly embedded and interconnected, access control needs to become an integral part of the resources being protected and be generated dynamically by the resources depending on the context in which they are being used. In this paper, we discuss a set of scenarios for access control needed in current and future systems and use that to argue that an approach for resources to generate and manage their access control policies dynamically on their own is needed. We discuss some approaches for generating such access control policies that may address the requirements of the scenarios.
Taylor P, Allpress S, Carr M, et al., 2018, Internet of Things: Realising the Potential of a Trusted Smart World, Internet of Things: Realising the Potential of a Trusted Smart World, London, Publisher: Royal Academy of Engineering: London
This report examines the policy challenges for the Internet of Things (IoT), and raises a broad range of issues that need to be considered if policy is to be effective and the potential economic value of IoT is harnessed. It builds on the Blackett review, The Internet of Things: making the most of the second digital revolution, adding detailed knowledge based on research from the PETRAS Cybersecurity of the Internet of Things Research Hub and input from Fellows of the Royal Academy of Engineering. The report targets government policymakers, regulators, standards bodies and national funding bodies, and will also be of interest to suppliers and adopters of IoT products and services.
Illiano VP, Paudice A, Munoz-Gonzalez L, et al., 2018, Determining Resilience Gains From Anomaly Detection for Event Integrity in Wireless Sensor Networks, ACM TRANSACTIONS ON SENSOR NETWORKS, Vol: 14, ISSN: 1550-4859
Muñoz-González L, Lupu EC, 2018, The secret of machine learning, ITNOW, Vol: 60, Pages: 38-39, ISSN: 1746-5702
Chizari H, Lupu E, Thomas P, 2018, Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world
© 2018 Institution of Engineering and Technology. All rights reserved. A physiological signal must have a certain level of randomness inside it to be a good source of randomness for generating cryptographic key. Dependency to the history is one of the measures to examine the strength of a randomness source. In dependency to the history, the adversary has infinite access to the history of generated random bits from the source and wants to predict the next random number based on that. Although many physiological signals have been proposed in literature as good source of randomness, no dependency to history analysis has been carried out to examine this fact. In this paper, using a large dataset of physiological signals collected from PhysioNet, the dependency to history of Interpuls Interval (IPI), QRS Complex, and EEG signals (including Alpha, Beta, Delta, Gamma and Theta waves) were examined. The results showed that despite the general assumption that the physiological signals are random, all of them are weak sources of randomness with high dependency to their history. Among them, Alpha wave of EEG signal shows a much better randomness and is a good candidate for post-processing and randomness extraction algorithm.
Turner HCM, Chizari H, Lupu E, 2018, Step intervals and arterial pressure in PVS schemes
© 2018 Institution of Engineering and Technology. All rights reserved. We build upon the idea of Physiological Value Based Security schemes as a means of securing body sensor networks (BSN). Such schemes provide a secure means for sensors in a BSN to communicate with one another, as long as they can measure the same underlying physiological signal. This avoids the use of pre-distributed keys and allows re-keying to be done easily. Such techniques require identifying signals and encoding methods that can be used in the scheme. Hence we first evaluate step interval as our physiological signal, using existing modular encoding method and our proposed learned partitioning function as the encoding methods. We show that both of these are usable with the scheme and identify a suitable parametrisation. We then go on to evaluate arterial blood pressure using our proposed learned mean FFT coefficients method. We demonstrate that with the correct parameters this could also be used in the scheme. This further improves the usability of PVS schemes, by identify two more signals that could be used, as well as two encoding methods that may also be useful for other signals.
Karafili E, Wang L, Kakas AC, et al., 2018, Helping forensic analysts to attribute cyber-attacks: An argumentation-based reasoner, Pages: 510-518, ISSN: 0302-9743
© Springer Nature Switzerland AG 2018. Discovering who performed a cyber-attack or from where it originated is essential in order to determine an appropriate response and future risk mitigation measures. In this work, we propose a novel argumentation-based reasoner for analyzing and attributing cyber-attacks that combines both technical and social evidence. Our reasoner helps the digital forensics analyst during the analysis of the forensic evidence by providing to the analyst the possible culprits of the attack, new derived evidence, hints about missing evidence, and insights about other paths of investigation. The proposed reasoner is flexible, deals with conflicting and incomplete evidence, and was tested on real cyber-attacks cases.
Karafili E, Spanaki K, Lupu EC, 2018, An argumentation reasoning approach for data processinge, COMPUTERS IN INDUSTRY, Vol: 94, Pages: 52-61, ISSN: 0166-3615
Steiner RV, Barrère MN, Lupu E, 2018, WSNs under attack! How bad is it? Evaluating connectivity impact using centrality measures
© 2018 Institution of Engineering and Technology. All rights reserved. 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.
Karafili E, Sgandurra D, Lupu E, 2018, A logic-based reasoner for discovering authentication vulnerabilities between interconnected accounts, Pages: 73-87, ISSN: 0302-9743
© Springer Nature Switzerland AG 2018. With users being more reliant on online services for their daily activities, there is an increasing risk for them to be threatened by cyber-attacks harvesting their personal information or banking details. These attacks are often facilitated by the strong interconnectivity that exists between online accounts, in particular due to the presence of shared (e.g., replicated) pieces of user information across different accounts. In addition, a significant proportion of users employs pieces of information, e.g. used to recover access to an account, that are easily obtainable from their social networks accounts, and hence are vulnerable to correlation attacks, where a malicious attacker is either able to perform password reset attacks or take full control of user accounts. This paper proposes the use of verification techniques to analyse the possible vulnerabilities that arises from shared pieces of information among interconnected online accounts. Our primary contributions include a logic-based reasoner that is able to discover vulnerable online accounts, and a corresponding tool that provides modelling of user accounts, their interconnections, and vulnerabilities. Finally, the tool allows users to perform security checks of their online accounts and suggests possible countermeasures to reduce the risk of compromise.
© Springer Nature Switzerland AG 2018. In recent years drones have become more widely used in military and non-military applications. Automation of these drones will become more important as their use increases. Individual drones acting autonomously will be able to achieve some tasks, but swarms of autonomous drones working together will be able to achieve much more complex tasks and be able to better adapt to changing environments. In this paper we describe an example scenario involving a swarm of drones from a military coalition and civil/humanitarian organisations that are working collaboratively to monitor areas at risk of flooding. We provide a definition of a swarm and how they can operate by exchanging messages. We define a flexible set of policies that are applicable to our scenario that can be easily extended to other scenarios or policy paradigms. These policies ensure that the swarms of drones behave as expected (e.g., for safety and security). Finally we discuss the challenges and limitations around policies for autonomous swarms and how new research, such as generative policies, can aid in solving these limitations.
Györgye A, Muñoz-González L, Gyorgy A, et al., 2018, Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection.
Chizari H, Lupu E, 2018, Extracting Randomness From The Trend of IPI for Cryptographic Operators in Implantable Medical Devices., CoRR, Vol: abs/1806.10984
Muñoz-González L, Biggio B, Demontis A, et al., 2017, Towards poisoning of deep learning algorithms with back-gradient optimization, Pages: 27-38
© 2017 Association for Computing Machinery. 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 data poisoning, i.e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process. To date, these attacks have been devised only against a limited class of binary learning algorithms, due to the inherent complexity of the gradient-based procedure used to optimize the poisoning points (a.k.a. adversarial training examples). In this work, we first extend the definition of poisoning attacks to multiclass problems. We then propose a novel poisoning algorithm based on the idea of back-gradient optimization, i.e., to compute the gradient of interest through automatic differentiation, while also reversing the learning procedure to drastically reduce the attack complexity. Compared to current poisoning strategies, our approach is able to target a wider class of learning algorithms, trained with gradient-based procedures, including neural networks and deep learning architectures. We empirically evaluate its effectiveness on several application examples, including spam filtering, malware detection, and handwritten digit recognition. We finally show that, similarly to adversarial test examples, adversarial training examples can also be transferred across different learning algorithms.
Munoz-Gonzalez L, Sgandurra D, Paudice A, et al., 2017, Efficient Attack Graph Analysis through Approximate Inference, ACM TRANSACTIONS ON PRIVACY AND SECURITY, Vol: 20, ISSN: 2471-2566
Illiano VP, Steiner RV, Lupu EC, 2017, Unity is strength! combining attestation and measurements inspection to handle malicious data injections in WSNs, Pages: 134-144
© 2017 Copyright held by the owner/author(s). Attestation and measurements inspection are different but complementary approaches towards the same goal: ascertaining the integrity of sensor nodes in wireless sensor networks. In this paper we compare the benefits and drawbacks of both techniques and seek to determine how to best combine them. However, our study shows that no single solution exists, as each choice introduces changes in the measurements collection process, affects the attestation protocol, and gives a diferent balance between the high detection rate of attestation and the low power overhead of measurements inspection. Therefore, we propose three strategies that combine measurements inspection and attestation in different ways, and a way to choose between them based on the requirements of different applications. We analyse their performance both analytically and in a simulator. The results show that the combined strategies can achieve a detection rate close to attestation, in the range 96-99%, whilst keeping a power overhead close to measurements inspection, in the range 1-10%.
Cullen A, Williams B, Bertino E, et al., 2017, Mission support for drones: A policy based approach, Pages: 7-12
© 2017 Copyright is held by the owner/author(s). We examine the impact of increasing autonomy on the use of airborne drones in joint operations by collaborative parties. As the degree of automation employed increases towards the level implied by the term 'autonomous', it becomes apparent that existing control mechanisms are insufficiently flexible. Using an architecture introduced by Bertino et al. in  and Verma et al. in , we consider the use of dynamic policy modification as a means to adjust to rapidly evolving scenarios. We show mechanisms which allow this approach to improve the effectiveness of operations without compromise to security or safety.
Karafili E, Lupu EC, 2017, Enabling data sharing in contextual environments: Policy representation and analysis, Pages: 231-238
© 2017 Association for Computing Machinery. Internet of Things environments enable us to capture more and more data about the physical environment we live in and about ourselves. The data enable us to optimise resources, personalise services and offer unprecedented insights into our lives. However, to achieve these insights data need to be shared (and sometimes sold) between organisations imposing rights and obligations upon the sharing parties and in accordance with multiple layers of sometimes conflicting legislation at international, national and organisational levels. In this work, we show how such rules can be captured in a formal representation called "Data Sharing Agreements". We introduce the use of abductive reasoning and argumentation based techniques to work with context dependent rules, detect inconsistencies between them, and resolve the inconsistencies by assigning priorities to the rules. We show how through the use of argumentation based techniques use-cases taken from real life application are handled flexibly addressing trade-offs between confidentiality, privacy, availability and safety.
Illiano VP, Munoz-Gonzalez L, Lupu EC, 2017, Don't fool Me!: Detection, Characterisation and Diagnosis of Spoofed and Masked Events in Wireless Sensor Networks, IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, Vol: 14, Pages: 279-293, ISSN: 1545-5971
Arunkumar S, Pipes S, Makaya C, et al., 2017, Next Generation Firewalls for Dynamic Coalitions, IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation Conference (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Publisher: IEEE
Karafili E, Pipes S, Lupu EC, 2017, Verification Techniques for Policy based Systems, IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation Conference (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Publisher: IEEE
Karafili E, Lupu EC, Arunkumar S, et al., 2017, Argumentation-based Policy Analysis for Drone Systems, IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation Conference (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Publisher: IEEE
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