Dr. Matthew Bradbury

Matthew Bradbury is a Lecturer conducting research into security, context privacy and trust assessment in resource-constrained and distributed systems.

Blog Posts

Poster: Multi-Layer Threat Analysis of the Cloud

Cloud systems are now highly pervasive and provide significant opportunities for adversaries to attack these systems. Therefore, there is a need to understand how attacks can be performed in a Cloud environment. It is important to perform this analysis for historical attacks — to analyse what happened and what alternate routes an adversary could have taken to reach their goals. However, it is also important to have a capability to perform speculative analysis in terms of what potential attacks could have been performed considering a specific set of vulnerabilities. Therefore, this work investigated modelling the dynamic cloud environment, threats to it and how an adversary could reach specific goals.

Poster: Effectiveness of Moving Target Defense Techniques to Disrupt Attacks in the Cloud

Cloud systems are now highly pervasive and provide significant opportunities for adversaries to attack these systems. One approach to mitigate the amount of time adversaries have to attack a system is to reconfigure the system, in essence to provide a moving target that adversaries need to contend with. The advantage of moving target defence (MTD) that adversaries have less time to perform reconnaissance and then attack a system. The moving target could be to change IP addresses — so adversaries are unsure of network structure, or to change the operating system and applications providing a service — so adversaries may struggle to exploit a vulnerability in specific software. Cloud systems are simultaneously well suited to MTD due to their ability to be dynamically reconfigured and challenging to apply MTD due to the wide variety of ways in which it can be employed. This means there is a need to be able to effectively select where MTDs should be applied in a cloud environment.

Attributes and Dimensions of Trust in Secure Systems

In this paper we set out our position on the concept of trust in secure systems. This position evolved from previous work investigating trust-based task offloading. The views in this paper intended to resolve issues that we perceived with the use of trust in literature produced by the security community. To resolve this we provided more general definitions for trust and related concepts, splitting them into the label that is assigned (trusted/trustworthy) and the measurements (trustiness/trustworthiness). We also identified a set of trust attributes that could be applied to this more general definition and explored dimensions along which attributes could be measured.

Towards Effective Performance Fuzzing

Fuzzing is a process that generates a large number of inputs to provide to a program with the intent to identify bugs. These inputs are generated by a fuzzer following some set of rules. Fuzzers typically focus on correctness bugs, however, also of importance are bugs that lead to poor performance. For example, bugs that cause a large number of hash collision can significantly reduce the performance of hash tables from amortized O(1) to O(n). This is a security vulnerability as the reduced performance can lead to loss of availability in applications. Examples of this type of vulnerability has occurred in the implementation of Python.

SlowCoach: Mutating Code to Simulate Performance Bugs

It is important that software runs fast. Better performance means that software is more responsive, consumes less energy, and provides less scope for adversaries to perform denial of service attacks that take advantage of poor performance. In order to identify performance issues, diagnosis tools need to be used to analyse software performance. Like other software, it is important that we are able to investigate the efficacy of performance diagnostic tools (such as perf). Mutation testing has been used to analyse the performance of a test suite’s ability to detect bugs in code. This is done by injecting mutations which leads to incorrect behaviour in the source code, and allows evaluation of the test suite’s capability to detect that inserted mutation. In this paper we extend the idea of mutation testing to performance mutation testing, in order to be able to test how well performance diagnostic tools are able to detect performance bugs.

Quantifying Source Location Privacy Routing Performance via Divergence and Information Loss

There has been much work investigating Source Location Privacy (SLP), including the analysis of techniques. However, one area in which there is a lack of analysis is against adversaries in the network. These adversaries can be cheaply equipped with a laptop, directional antenna and a cheap software defined radio to effectively locate the sources of valuable assets. In this work we investigated how to quantify the amount of information a non-SLP-aware routing matrix reveals to an adversary compared to a SLP-aware routing matrix via a measure of divergence. Using this measure an algorithm was developed to transform a non-SLP-aware routing matrix into an SLP-aware routing matrix.

Information Management for Trust Computation on Resource-constrained IoT Devices

There is increasing interest in using highly resource-constrained IoT devices to perform complex tasks. These resource might include limited processing power (e.g., 32MHz CPU), RAM (e.g., 32 KiB to 256 KiB), ROM (512 KiB), and potentially no stable storage. However, because of the limited resources an IoT device may need to offload expensive tasks to resource-rich devices. These might be a Cloud server or an Edge node if the latency of task responses is important. These Cloud or Edge servers may not always perform tasks correctly or timely. One approach in deciding who to select is to calculate a value of behavioural trust which acts as a predictor for whether the Cloud or Edge will behave well. This could be via a proactive trust assessment, or a reactive trust assessment. However, the same limited resources that necessitate offloading tasks also means that there needs to be a strategy to decide which historical information used to calculate behavioural trust should be kept in memory and which information should be discarded when memory is full.

Threat-Modeling-Guided Trust-Based Task Offloading for Resource-Constrained Internet of Things

There is increasing interest in using highly resource-constrained IoT devices to perform complex tasks. These resource might include limited processing power (e.g., 32MHz CPU), RAM (e.g., 32 KiB to 256 KiB), ROM (512 KiB), and potentially no stable storage. However, because of the limited resources an IoT device may need to offload expensive tasks to resource-rich devices. These might be a Cloud server or an Edge node if the latency of task responses is important. In order to address this issue, in this work we developed a middleware to facilitate task offloading using a measure of behavioural trust. In addition, we also performed a threat modelling on the system to identify ways in which malicious Edge nodes could attempt to manipulate reputation in order to hide their bad actions and ways in which this could be mitigated.

February 2022 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

Challenges Porting to the nRF52840

I have been working with Giovanni (a visiting PhD student) to port code from a previous project from the Zolertia RE-Mote platform to new nRF52840 DK boards. These boards are much more capable than the Zolertia RE-Motes, not just in terms of computational capability, but also in terms of the ability to debug and instrument them. While we expected to encounter some challenges with the port, the most problematic and time consuming issues ended up being entirely unexpected.

January 2022 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

December 2021 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

November 2021 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

October 2021 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

September 2021 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

August 2021 Reading List

A collection of interesting news articles, features, publications, projects and source code that I find interesting.

Trust Trackers for Computation Offloading in Edge-Based IoT Networks

There is increasing interest in using highly resource-constrained IoT devices to perform complex tasks. These resource might include limited processing power (e.g., 32MHz CPU), RAM (e.g., 32 KiB to 256 KiB), ROM (512 KiB), and potentially no stable storage. However, because of the limited resources an IoT device may need to offload expensive tasks to resource-rich devices. These might be a Cloud server or an Edge node if the latency of task responses is important. In most cases, trust is built up reactively where an interaction is performed and the result of that interaction is used to update a trust model. In this work we instead adopt a proactive approach to assessing trust, where a challenge is periodically sent to each resource-rich device that a task could be offloaded to. This challenge is sufficiently expensive for the resource-rich device to compute a result, but cheap for the resource-constrained device to verify.

Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes

There is increasing interest in using highly resource-constrained IoT devices to perform complex tasks. These resource might include limited processing power (e.g., 32MHz CPU), RAM (e.g., 32 KiB to 256 KiB), ROM (512 KiB), and potentially no stable storage. However, because of the limited resources an IoT device may need to offload expensive tasks to resource-rich devices. These might be a Cloud server or an Edge node if the latency of task responses is important. In order to address this issue, in this work we developed a middleware to facilitate task offloading using a measure of behavioural trust.

A Spatial Source Location Privacy-Aware Duty Cycle for Internet of Things Sensor Networks

Source Location Privacy (SLP) is an important problem when monitoring valuable assets with wireless sensors. It is important that sensitive context information, such as the location of an asset, is not revealed to adversaries. These wireless sensors are typically deployed with a limited energy source, so protection approaches need to consider their energy cost. In order to save energy, applications deployed on these devices perform duty cycling, where they aim to spend the majority of their lifetime sleeping. However, arbitrary duty cycling algorithms can lead to delays in messages being sent and received. For SLP algorithms that involve time sensitive messages an arbitrary duty cycle will impact the ability to provide SLP. So this paper proposed a duty cycling algorithm that uses knowledge of the SLP protocol to calculate when to wake up and when to sleep.

Privacy Challenges with Protecting Live Vehicular Location Context

Previous work on live location privacy protection for vehicle-to-vehicle (V2V) communication has rarely considered multiple sources of identity leakage. This is problematic as vehicles will exhibit multiple sources of identifying information. However, not all of this information will be available to all threat actors. Some identifying information will be easier and cheaper to obtain (e.g., via a sensor network listening for V2V communications) compared to other approaches (e.g., deploying a network of cameras). This paper identified the numerous sources of identity leakage from a vehicle and devices which may be present in it, the threat actors trying to violate the vehicle’s live location privacy, the techniques used to protect privacy, and ways in which identity sources and protection mechanisms can interact.

PNT Cyber Resilience: a Lab2Live Observer Based Approach, Report 2: Specifications for Cyber Testing Facilities

Global navigation satellite systems (GNSS) such as GPS and Galileo are vital sources of positioning, navigation and timing (PNT) information for vehicles. This information is of critical importance for connected autonomous vehicles (CAVs) due to their dependence on this information for localisation, route planning, and situational awareness. A downside to solely relying on GNSS for PNT is that the signal strength arriving from navigation satellites in space is weak and currently there is no authentication included in the civilian GNSS adopted in the automotive industry. This means that cyber-attacks against the GNSS signal via jamming or spoofing are attractive to adversaries due to the potentially high impact they can achieve. This report introduces specifications and recommendations for GNSS cyber-security test facilities for CAVs. These specifications are based on a survey of academic literature, interviews with a select group of experts, and experiences obtained performing laboratory and real-world testing.

PNT Cyber Resilience: a Lab2Live Observer Based Approach, Report 1: GNSS Resilience and Identified Vulnerabilities

Global navigation satellite systems (GNSS) such as GPS and Galileo are vital sources of positioning, navigation and timing (PNT) information for vehicles. This information is of critical importance for connected autonomous vehicles (CAVs) due to their dependence on this information for localisation, route planning, and situational awareness. A downside to solely relying on GNSS for PNT is that the signal strength arriving from navigation satellites in space is weak and currently there is no authentication included in the civilian GNSS adopted in the automotive industry. This means that cyber-attacks against the GNSS signal via jamming or spoofing are attractive to adversaries due to the potentially high impact they can achieve. This report reviews the vulnerabilities of GNSS services for CAVs, as well as detection and mitigation techniques, summarises the opinions on PNT cybertesting sourced from a select group of experts, and finishes with a description of the associated lab-based and real-world feasibility study and proposed research methodology.

The Impact of Decreasing Transmit Power Levels on FlockLab To Achieve a Sparse Network

When developing techniques for IoT devices, it is preferable to perform testing on real devices in real situations such as on FlockLab 2, FIT IoT-LAB, and INDRIYA 2, as simulation will not perfectly reproduce these environments. However, IoT testbeds are not always deployed in the same scenarios for which techniques are developed for. It is often the case that testbeds are setup in existing facilities (such as inside office buildings) and devices are positioned close to each other. This means that there is currently a lack of testbeds supporting the testing of applications that will be deployed outside, on a large scale, with sparse connectivity. Commonly, the transmission power of device can be reduced in order to emulate a sparse network, this paper looked at the baseline effects of doing so.

A Near-Optimal Source Location Privacy Scheme for Wireless Sensor Networks

Source Location Privacy (SLP) is an important problem when monitoring valuable assets with wireless sensors. It is important that sensitive context information, such as the location of an asset, is not revealed to adversaries. This work aimed to investigate optimal strategies to provide SLP by formulating the routing problem using integer linear programming (ILP). IBM’s ILOG CPLEX was used to obtain an optimal solution to the model. This solution aimed to delay and group messages until as late as possible and then deliver the messages to their destination. However, this solution made the assumption that wireless communication is perfectly reliable, which is not the case. So the optimal solution was recreated by a near-optimal routing algorithm that aimed to produce similar behaviour.