Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

e.c.lupu Website

 
 
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Location

 

564Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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226 results found

Chizari H, Lupu EC, 2019, Extracting randomness from the trend of IPI for cryptographic operators in implantable medical devices, IEEE Transactions on Dependable and Secure Computing, ISSN: 1545-5971

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 such as in St. Jude Medical's Implantable Cardiac Devices, Johnson and Johnson insulin pumps and vulnerabilities in brain Neuro-implants. The use of asymmetric cryptography in particular is not a practical solution for IMDs due to the scarce computational and power resources, symmetric key cryptography is preferred. One of the factors in security of a symmetric cryptographic system is to use a strong key for encryption. A solution to develop such a strong key without using extensive resources in an IMD, is to extract it from the body physiological signals. In order to have a strong enough key, the physiological signal must be a strong source of randomness and InterPulse Interval (IPI) has been advised to be such that. A strong randomness source should have five conditions: Universality (available on all people), Liveness (available at any-time), Robustness (strong random number), Permanence (independent from its history) and Uniqueness (independent from other sources). Nevertheless, for current proposed random extraction methods from IPI these conditions (mainly last three conditions) were not examined. In this study, firstly, we proposed a methodology to measure the last three conditions: Information secrecy measures for Robustness, Santha-Vazirani Source delta value for Permanence and random sources dependency analysis for Uniqueness. Then, using a huge dataset of IPI values (almost 900,000,000 IPIs), we showed that IPI does not have conditions of Robustness and Permanence as a randomness source. Thus, extraction of a strong uniform random number from IPI value, mathematically, is impossible. Thirdly, rather than using the value of IPI, we proposed the trend of IPI as a source for a new randomness extraction method named as Martingale Randomness Extraction from IPI (MRE-IPI). We evaluat

Journal article

Collinge G, Lupu E, Munoz Gonzalez L, 2019, Defending against Poisoning Attacks in Online Learning Settings, European Symposium on Artificial Neural Networks, Publisher: ESANN

Machine learning systems are vulnerable to data poisoning, acoordinated attack where a fraction of the training dataset is manipulatedby an attacker to subvert learning. In this paper we first formulate an optimal attack strategy against online learning classifiers to assess worst-casescenarios. We also propose two defence mechanisms to mitigate the effectof online poisoning attacks by analysing the impact of the data points inthe classifier and by means of an adaptive combination of machine learning classifiers with different learning rates. Our experimental evaluationsupports the usefulness of our proposed defences to mitigate the effect ofpoisoning attacks in online learning settings.

Conference paper

Steiner RV, Lupu E, 2019, Towards more practical software-based attestation, Computer Networks, Vol: 149, Pages: 43-55, ISSN: 1389-1286

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).

Journal article

Karafili E, Spanaki K, Lupu E, 2019, Access Control and Quality Attributes of Open Data: Applications and Techniques, Workshop on Quality of Open Data, Publisher: Springer Verlag (Germany), Pages: 603-614, ISSN: 1865-1348

Open Datasets provide one of the most popular ways to ac- quire 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.

Conference paper

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.

Book chapter

Munoz Gonzalez L, Lupu E, 2019, The Security of Machine Learning Systems, AI in Cybersecurity, Editors: Sikos

Book chapter

Karafili E, Sgandurra D, Lupu E, A logic-based reasoner for discovering authentication vulnerabilities between interconnected accounts, 1st International Workshop on Emerging Technologies for Authorization and Authentication, Publisher: Springer Verlag, ISSN: 0302-9743

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 ac- counts, 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.

Conference paper

Cullen A, Karafili E, Pilgrim A, Williams C, Lupu Eet al., Policy support for autonomous swarms of drones, 1st International Workshop on Emerging Technologies for Authorization and Authentication, Publisher: Springer Verlag, ISSN: 0302-9743

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.

Conference paper

Karafili E, Wang L, Kakas A, Lupu Eet al., 2018, Helping forensic analysts to attribute cyber-attacks: an argumentation-based reasoner, International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2018), Publisher: Springer Verlag, Pages: 510-518, ISSN: 0302-9743

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.

Conference paper

Paudice A, Muñoz-González L, Lupu EC, 2018, Label sanitization against label flipping poisoning attacks, Nemesis'18. Workshop in Recent Advances in Adversarial Machine Learning, Publisher: Springer Verlag, ISSN: 0302-9743

Many machine learning systems rely on data collected in thewild from untrusted sources, exposing the learning algorithms to datapoisoning. Attackers can inject malicious data in the training datasetto subvert the learning process, compromising the performance of thealgorithm producing errors in a targeted or an indiscriminate way. Labelflipping attacks are a special case of data poisoning, where the attackercan control the labels assigned to a fraction of the training points. Evenif the capabilities of the attacker are constrained, these attacks havebeen shown to be effective to significantly degrade the performance ofthe system. In this paper we propose an efficient algorithm to performoptimal label flipping poisoning attacks and a mechanism to detect andrelabel suspicious data points, mitigating the effect of such poisoningattacks.

Conference paper

Spanaki K, Gürgüç Z, Mulligan C, Lupu ECet al., Organizational Cloud Security and Control: a Proactive Approach, Information Technology and People, ISSN: 0959-3845

Journal article

Arunkumar S, Pipes S, Makaya C, Bertino E, Karafili E, Lupu E, Williams Cet al., 2018, Next generation firewalls for dynamic coalitions, DAIS Workshop, 2017 IEEE SmartWorld Congress, Publisher: IEEE

Firewalls represent a critical security building block for networks as they monitor and control incoming and outgoing network traffic based on the enforcement of predetermined secu- rity rules, referred to as firewall rules. Firewalls are constantly being improved to enhance network security. From being a simple filtering device, firewall has been evolved to operate in conjunc- tion in intrusion detection and prevention systems. This paper reviews the existing firewall policies and assesses their application in highly dynamic networks such as coalitions networks. The paper also describe the need for the next-generation firewall policies and how the generative policy model can be leveraged.

Conference paper

Calo S, Verma D, Chakraborty S, Bertino E, Lupu E, Cirincione Get al., 2018, Self-generation of access control policies, Pages: 39-47

© 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.

Conference paper

Rashid A, Danezis G, Chivers H, Lupu E, Martin A, Lewis M, Peersman Cet al., 2018, Scoping the Cyber Security Body of Knowledge, IEEE SECURITY & PRIVACY, Vol: 16, Pages: 96-102, ISSN: 1540-7993

Journal article

Steiner RV, Barrère M, Lupu E, 2018, WSNs Under Attack! How Bad Is It? Evaluating Connectivity Impact Using Centrality Measures, Living in the Internet of Things: Cybersecurity of the IoT - 2018

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.

Conference paper

Turner HCM, Chizari H, Lupu E, 2018, Step intervals and arterial pressure in PVS schemes, Living in the Internet of Things: Cybersecurity of the IoT - 2018, Publisher: Institution of Engineering and Technology, Pages: 36-45

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.

Conference paper

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, Living in the Internet of Things: Cybersecurity of the IoT - 2018, Publisher: Institution of Engineering and Technology

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.

Conference paper

Taylor P, Allpress S, Carr M, Lupu E, Norton J, Smith L, Blackstock J, Boyes H, Hudson-Smith A, Brass I, Chizari H, Cooper R, Coultron P, Craggs B, Davies N, De Roure D, Elsden M, Huth M, Lindley J, Maple C, Mittelstadt B, Nicolescu R, Nurse J, Procter R, Radanliev P, Rashid A, Sgandurra D, Skatova A, Taddeo M, Tanczer L, Vieira-Steiner R, Watson JDM, Wachter S, Wakenshaw S, Carvalho G, Thompson RJ, Westbury PSet 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.

Report

Munoz Gonzalez L, Lupu E, 2018, The secret of machine learning, ITNOW, Vol: 60, Pages: 38-39, ISSN: 1746-5702

Luis Muñoz-González and Emil C. Lupu, from Imperial College London, explore the vulnerabilities of machine learning algorithms.

Journal article

Illiano V, Lupu E, Muñoz-González L, Paudice APet 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

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.

Journal article

Calo S, Lupu E, Bertino E, Arunkumar S, Cirincione G, Rivera B, Cullen Aet al., 2018, Research Challenges in Dynamic Policy-Based Autonomous Security, IEEE International Conference on Big Data (IEEE Big Data), Publisher: IEEE, Pages: 2970-2973

Conference paper

Barrere M, Lupu EC, 2017, Naggen: a Network Attack Graph GENeration tool, 2017 IEEE Conference on Communications and Network Security, CNS 2017, Publisher: IEEE, Pages: 378-379

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.

Conference paper

Karafili E, Lupu E, Cullen A, Williams B, Arunkumar S, Calo Set al., Improving Data Sharing in Data Rich Environments, 1st IEEE Big Data International Workshop on Policy-based Autonomic Data Governance, IEEE BigData, Publisher: IEEE

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.

Conference paper

Karafili E, Spanaki K, Lupu E, 2017, An Argumentation Reasoning Approach for Data Processing, Computers in Industry, Vol: 94, Pages: 52-61, ISSN: 0166-3615

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.

Journal article

Muñoz-González L, Biggio B, Demontis A, Paudice A, Wongrassamee V, Lupu EC, Roli Fet 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.

Conference paper

Muñoz-González L, Biggio B, Demontis A, Paudice A, Wongrassamee V, Lupu EC, Roli Fet al., 2017, Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization., CoRR, Vol: abs/1708.08689

Journal article

Barrere Cambrun M, Vieira Steiner R, Mohsen R, Lupu Eet al., Tracking the Bad Guys: An Efficient Forensic Methodology To Trace Multi-step Attacks Using Core Attack Graphs, 13th International Conference on Network and Service Management (CNSM'17)

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.

Conference paper

Munoz Gonzalez L, Lupu E, Bayesian Attack Graphs for Security Risk Assessment, IST-153 NATO Workshop on Cyber Resilience

Conference paper

Muñoz-González L, Sgandurra D, Paudice A, Lupu ECet al., 2017, Efficient Attack Graph Analysis through Approximate Inference, ACM Transactions on Privacy and Security, Vol: 20, ISSN: 2471-2566

Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system's components given their vulnerabilities and interconnections, and accounts for multi-step attacks spreading through the system. Whilst static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, e.g. from SIEM software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this paper we show how Loopy Belief Propagation - an approximate inference technique - can be applied to attack graphs, and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm's accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.

Journal article

Illiano V, Steiner RV, Lupu EC, 2017, Unity is strength! Combining attestation and measurements inspection to handle malicious data injections in WSNs, Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) 2017, Publisher: ACM, Pages: 134-144

AŠestation and measurements inspection are di‚erent but com-plementary approaches towards the same goal: ascertaining theintegrity of sensor nodes in wireless sensor networks. In this paperwe compare the bene€ts and drawbacks of both techniques and seekto determine how to best combine them. However, our study showsthat no single solution exists, as each choice introduces changesin the measurements collection process, a‚ects the aŠestation pro-tocol, and gives a di‚erent balance between the high detectionrate of aŠestation and the low power overhead of measurementsinspection. Œerefore, we propose three strategies that combinemeasurements inspection and aŠestation in di‚erent ways, and away to choose between them based on the requirements of di‚erentapplications. We analyse their performance both analytically andin a simulator. Œe results show that the combined strategies canachieve a detection rate close to aŠestation, in the range 96-99%,whilst keeping a power overhead close to measurements inspection,in the range 1-10%.

Conference paper

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