Vulnerability assessment of modern ICT infrastructure from an information warfare perspective.

Ph. D. University of KwaZulu-Natal, Durban 2011.The overall objective of the study is to provide a vulnerability assessment of the mobile communications infrastructure to information warfare attacks; this study has a South African focus. The mobile infrastructure was selected as the infrastructure and mobile devices incorporate the majority of modern ICT technologies, namely social networking, wireless connectivity and mobility, mass storage, as well as the telecommunications elements. The objectives of the study are to: Propose a new information warfare model, and from this deduce a vulnerability assessment framework from the specific information warfare perspective. These are the guiding frameworks and model for the study. Gather information regarding threats and vulnerabilities, with particular focus on potential use in information warfare and relevance to South Africa. Establish the criticality of the mobile infrastructure in South Africa. Use the gathered information in the vulnerability assessment, to assess the vulnerability of the mobile infrastructure and related devices and services. The model and framework are generated through desk-based research. The information is gathered from research protocols that are relevant to both research and risk and vulnerability assessment, these include: expert input through interviews and a research workshop, incident and trend analyses through news and vendor reports and academic publishing, computer simulation, questionnaire survey, and mathematical analyses. The information is then triangulated by using it in the vulnerability assessment. The primary and secondary data shows that attacks on confidentiality are the most prevalent for both computer-based networks and the mobile infrastructure. An increase in threats and incidents for both computer and mobile platforms is being seen. The information security trends in South Africa indicate that the existing security concerns are likely to worsen, in particular the high infection rates. The research indicates that the mobile infrastructure is critical in South Africa. The study validates the proposed framework, which indicates that South Africa is vulnerable to an information warfare attack in general. Key aspects of vulnerability in the mobile infrastructure are highlighted; the apparent high load of the mobile infrastructure in South Africa can be seen as a high risk vulnerability. Suggestions to mitigate vulnerabilities and threats are provided

Towards trustworthy computing on untrustworthy hardware

Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

Design of Discrete-time Chaos-Based Systems for Hardware Security Applications

Security of systems has become a major concern with the advent of technology. Researchers are proposing new security solutions every day in order to meet the area, power and performance specifications of the systems. The additional circuit required for security purposes can consume significant area and power. This work proposes a solution which utilizes discrete-time chaos-based logic gates to build a system which addresses multiple hardware security issues. The nonlinear dynamics of chaotic maps is leveraged to build a system that mitigates IC counterfeiting, IP piracy, overbuilding, disables hardware Trojan insertion and enables authentication of connecting devices (such as IoT and mobile). Chaos-based systems are also used to generate pseudo-random numbers for cryptographic applications.The chaotic map is the building block for the design of discrete-time chaos-based oscillator. The analog output of the oscillator is converted to digital value using a comparator in order to build logic gates. The logic gate is reconfigurable since different parameters in the circuit topology can be altered to implement multiple Boolean functions using the same system. The tuning parameters are control input, bifurcation parameter, iteration number and threshold voltage of the comparator. The proposed system is a hybrid between standard CMOS logic gates and reconfigurable chaos-based logic gates where original gates are replaced by chaos-based gates. The system works in two modes: logic locking and authentication. In logic locking mode, the goal is to ensure that the system achieves logic obfuscation in order to mitigate IC counterfeiting. The secret key for logic locking is made up of the tuning parameters of the chaotic oscillator. Each gate has 10-bit key which ensures that the key space is large which exponentially increases the computational complexity of any attack. In authentication mode, the aim of the system is to provide authentication of devices so that adversaries cannot connect to devices to learn confidential information. Chaos-based computing system is susceptible to process variation which can be leveraged to build a chaos-based PUF. The proposed system demonstrates near ideal PUF characteristics which means systems with large number of primary outputs can be used for authenticating devices

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Acoustic-channel attack and defence methods for personal voice assistants

Personal Voice Assistants (PVAs) are increasingly used as interface to digital environments. Voice commands are used to interact with phones, smart homes or cars. In the US alone the number of smart speakers such as Amazon’s Echo and Google Home has grown by 78% to 118.5 million and 21% of the US population own at least one device. Given the increasing dependency of society on PVAs, security and privacy of these has become a major concern of users, manufacturers and policy makers. Consequently, a steep increase in research efforts addressing security and privacy of PVAs can be observed in recent years. While some security and privacy research applicable to the PVA domain predates their recent increase in popularity and many new research strands have emerged, there lacks research dedicated to PVA security and privacy. The most important interaction interface between users and a PVA is the acoustic channel and acoustic channel related security and privacy studies are desirable and required. The aim of the work presented in this thesis is to enhance the cognition of security and privacy issues of PVA usage related to the acoustic channel, to propose principles and solutions to key usage scenarios to mitigate potential security threats, and to present a novel type of dangerous attack which can be launched only by using a PVA alone. The five core contributions of this thesis are: (i) a taxonomy is built for the research domain of PVA security and privacy issues related to acoustic channel. An extensive research overview on the state of the art is provided, describing a comprehensive research map for PVA security and privacy. It is also shown in this taxonomy where the contributions of this thesis lie; (ii) Work has emerged aiming to generate adversarial audio inputs which sound harmless to humans but can trick a PVA to recognise harmful commands. The majority of work has been focused on the attack side, but there rarely exists work on how to defend against this type of attack. A defence method against white-box adversarial commands is proposed and implemented as a prototype. It is shown that a defence Automatic Speech Recognition (ASR) can work in parallel with the PVA’s main one, and adversarial audio input is detected if the difference in the speech decoding results between both ASR surpasses a threshold. It is demonstrated that an ASR that differs in architecture and/or training data from the the PVA’s main ASR is usable as protection ASR; (iii) PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has limited control over this process when a PVA is triggered without user’s intent or a PVA belongs to others. A user is unable to control the recording behaviour of surrounding PVAs, unable to signal privacy requirements and unable to track conversation recordings. An acoustic tagging solution is proposed aiming to embed additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken if necessary. A prototype tagging device based on PocketSphinx is implemented. Using Google Home Mini as the PVA, it is demonstrated that the device can tag conversations and the tagging signal can be retrieved from conversations stored in the Google back-end system; (iv) Acoustic tagging provides users the capability to signal their permission to the back-end PVA service, and another solution inspired by Denial of Service (DoS) is proposed as well for protecting user privacy. Although PVAs are very helpful, they are also continuously monitoring conversations. When a PVA detects a wake word, the immediately following conversation is recorded and transported to a cloud system for further analysis. An active protection mechanism is proposed: reactive jamming. A Protection Jamming Device (PJD) is employed to observe conversations. Upon detection of a PVA wake word the PJD emits an acoustic jamming signal. The PJD must detect the wake word faster than the PVA such that the jamming signal still prevents wake word detection by the PVA. An evaluation of the effectiveness of different jamming signals and overlap between wake words and the jamming signals is carried out. 100% jamming success can be achieved with an overlap of at least 60% with a negligible false positive rate; (v) Acoustic components (speakers and microphones) on a PVA can potentially be re-purposed to achieve acoustic sensing. This has great security and privacy implication due to the key role of PVAs in digital environments. The first active acoustic side-channel attack is proposed. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smartphone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movement can be monitored to steal Android unlock patterns. The number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 phone can be reduced by up to 70% using this novel unnoticeable acoustic side-channel

Coding for Privacy in Distributed Computing

I et distribuert datanettverk samarbeider flere enheter for å løse et problem. Slik kan vi oppnå mer enn summen av delene: samarbeid gjør at problemet kan løses mer effektivt, og samtidig blir det mulig å løse problemer som hver enkelt enhet ikke kan løse på egen hånd. På den annen side kan enheter som bruker veldig lang tid på å fullføre sin oppgave øke den totale beregningstiden betydelig. Denne såkalte straggler-effekten kan oppstå som følge av tilfeldige hendelser som minnetilgang og oppgaver som kjører i bakgrunnen på de ulike enhetene. Straggler-problemet blokkerer vanligvis hele beregningen siden alle enhetene må vente på at de treigeste enhetene blir ferdige. Videre kan deling av data og delberegninger mellom de ulike enhetene belaste kommunikasjonsnettverket betydelig. Spesielt i et trådløst nettverk hvor enhetene må dele en enkelt kommunikasjonskanal, for eksempel ved beregninger langs kanten av et nettverk (såkalte kantberegninger) og ved føderert læring, blir kommunikasjonen ofte flaskehalsen. Sist men ikke minst gir deling av data med upålitelige enheter økt bekymring for personvernet. En som ønsker å bruke et distribuert datanettverk kan være skeptisk til å dele personlige data med andre enheter uten å beskytte sensitiv informasjon tilstrekkelig. Denne avhandlingen studerer hvordan ideer fra kodeteori kan dempe straggler-problemet, øke effektiviteten til kommunikasjonen og garantere datavern i distribuert databehandling. Spesielt gir del A en innføring i kantberegning og føderert læring, to populære instanser av distribuert databehandling, lineær regresjon, et vanlig problem som kan løses ved distribuert databehandling, og relevante ideer fra kodeteori. Del B består av forskningsartikler skrevet innenfor rammen av denne avhandlingen. Artiklene presenterer metoder som utnytter ideer fra kodeteori for å redusere beregningstiden samtidig som datavernet ivaretas ved kantberegninger og ved føderert læring. De foreslåtte metodene gir betydelige forbedringer sammenlignet med tidligere metoder i litteraturen. For eksempel oppnår en metode fra artikkel I en 8%-hastighetsforbedring for kantberegninger sammenlignet med en nylig foreslått metode. Samtidig ivaretar vår metode datavernet, mens den metoden som vi sammenligner med ikke gjør det. Artikkel II presenterer en metode som for noen brukstilfeller er opp til 18 ganger raskere for føderert læring sammenlignet med tidligere metoder i litteraturen.In a distributed computing network, multiple devices combine their resources to solve a problem. Thereby the network can achieve more than the sum of its parts: cooperation of the devices can enable the devices to compute more efficiently than each device on its own could and even enable the devices to solve a problem neither of them could solve on its own. However, devices taking exceptionally long to finish their tasks can exacerbate the overall latency of the computation. This so-called straggler effect can arise from random effects such as memory access and tasks running in the background of the devices. The effect typically stalls the whole network because most devices must wait for the stragglers to finish. Furthermore, sharing data and results among devices can severely strain the communication network. Especially in a wireless network where devices have to share a common channel, e.g., in edge computing and federated learning, the communication links often become the bottleneck. Last but not least, offloading data to untrusted devices raises privacy concerns. A participant in the distributed computing network might be weary of sharing personal data with other devices without adequately protecting sensitive information. This thesis analyses how ideas from coding theory can mitigate the straggler effect, reduce the communication load, and guarantee data privacy in distributed computing. In particular, Part A gives background on edge computing and federated learning, two popular instances of distributed computing, linear regression, a common problem to be solved by distributed computing, and the specific ideas from coding theory that are proposed to tackle the problems arising in distributed computing. Part B contains papers on the research performed in the framework of this thesis. The papers propose schemes that combine the introduced coding theory ideas to minimize the overall latency while preserving data privacy in edge computing and federated learning. The proposed schemes significantly outperform state-of-the-art schemes. For example, a scheme from Paper I achieves an 8% speed-up for edge computing compared to a recently proposed non-private scheme while guaranteeing data privacy, whereas the schemes from Paper II achieve a speed-up factor of up to 18 for federated learning compared to current schemes in the literature for considered scenarios.Doktorgradsavhandlin

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area