SoK: Acoustic Side Channels

We provide a state-of-the-art analysis of acoustic side channels, cover all the significant academic research in the area, discuss their security implications and countermeasures, and identify areas for future research. We also make an attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.Comment: 16 page

Evaluating the Efficacy of Implicit Authentication Under Realistic Operating Scenarios

Smartphones contain a wealth of personal and corporate data. Several surveys have reported that about half of the smartphone owners do not configure primary authentication mechanisms (such as PINs, passwords, and fingerprint- or facial-recognition systems) on their devices to protect data due to usability concerns. In addition, primary authentication mechanisms have been subject to operating system flaws, smudge attacks, and shoulder surfing attacks. These limitations have prompted researchers to develop implicit authentication (IA), which authenticates a user by using distinctive, measurable patterns of device use that are gathered from the device users without requiring deliberate actions. Researchers have claimed that IA has desirable security and usability properties and it seems a promising candidate to mitigate the security and usability issues of primary authentication mechanisms. Our observation is that the existing evaluations of IA have a preoccupation with accuracy numbers and they have neglected the deployment, usability and security issues that are critical for its adoption. Furthermore, the existing evaluations have followed an ad-hoc approach based on synthetic datasets and weak adversarial models. To confirm our observations, we first identify a comprehensive set of evaluation criteria for IA schemes. We gather real-world datasets and evaluate diverse and prominent IA schemes to question the efficacy of existing IA schemes and to gain insight into the pitfalls of the contemporary evaluation approach to IA. Our evaluation confirms that under realistic operating conditions, several prominent IA schemes perform poorly across key evaluation metrics and thereby fail to provide adequate security. We then examine the usability and security properties of IA by carefully evaluating promising IA schemes. Our usability evaluation shows that the users like the convenience offered by IA. However, it uncovers issues due to IA's transparent operation and false rejects, which are both inherent to IA. It also suggests that detection delay and false accepts are concerns to several users. In terms of security, our evaluation based on a realistic, stronger adversarial model shows the susceptibility of highly accurate, touch input-based IA schemes to shoulder surfing attacks and attacks that train an attacker by leveraging raw touch data of victims. These findings exemplify the significance of realistic adversarial models. These critical security and usability challenges remained unidentified by the previous research efforts due to the passive involvement of human subjects (only as behavioural data sources). This emphasizes the need for rapid prototyping and deployment of IA for an active involvement of human subjects in IA research. To this end, we design, implement, evaluate and release in open source a framework, which reduces the re-engineering effort in IA research and enables deployment of IA on off-the-shelf Android devices. The existing authentication schemes available on contemporary smartphones fail to provide both usability and security. Authenticating users based on their behaviour, as suggested by the literature on IA, is a promising idea. However, this thesis concludes that several results reported in the existing IA literature are misleading due to the unrealistic evaluation conditions and several critical challenges in the IA domain need yet to be resolved. This thesis identifies these challenges and provides necessary tools and design guidelines to establish the future viability of IA

The Proceedings of 15th Australian Information Security Management Conference, 5-6 December, 2017, Edith Cowan University, Perth, Australia

Conference Foreword The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fifteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The papers cover topics from vulnerabilities in “Internet of Things” protocols through to improvements in biometric identification algorithms and surveillance camera weaknesses. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Twenty two papers were submitted from Australia and overseas, of which eighteen were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conference. To our sponsors, also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

Adversarial robustness of deep learning enabled industry 4.0 prognostics

The advent of Industry 4.0 in automation and data exchange leads us toward a constant evolution in smart manufacturing environments, including extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Specifically, the state-of-the-art Prognostics and Health Management (PHM) has shown great success in achieving a competitive edge in Industry 4.0 by reducing maintenance cost, downtime, and increasing productivity by making data-driven informed decisions. These state-of-the-art PHM systems employ IoT device data and DL algorithms to make informed decisions/predictions of Remaining Useful Life (RUL). Unfortunately, IoT sensors and DL algorithms, both are prone to cyber-attacks. For instance, deep learning algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks have been extensively studied in the computer vision domain. However, it is surprising that their impact on the PHM domain is yet not explored. Thus, modern data-driven intelligent PHM systems pose a significant threat to safety- and cost-critical applications. Towards this, in this thesis, we propose a methodology to design adversarially robust PHM systems by analyzing the effect of different types of adversarial attacks on several DL enabled PHM models. More specifically, we craft adversarial attacks using Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and evaluate their impact on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Bi-directional LSTM, and Multi-layer perceptron (MLP) based PHM models using the proposed methodology. The obtained results using NASA's turbofan engine, and a well-known battery PHM dataset show that these systems are vulnerable to adversarial attacks and can cause a serious defect in the RUL prediction. We also analyze the impact of adversarial training using the proposed methodology to enhance the adversarial robustness of the PHM systems. The obtained results show that adversarial training is successful in significantly improvising the robustness of these PHM models.Includes bibliographical references (pages 80-98)

On the security of mobile sensors

PhD ThesisThe age of sensor technology is upon us. Sensor-rich mobile devices are ubiquitous. Smart-phones, tablets, and wearables are increasingly equipped with sensors such as GPS, accelerometer, Near Field Communication (NFC), and ambient sensors. Data provided by such sensors, combined with the fast-growing computational capabilities on mobile platforms, offer richer and more personalised apps. However, these sensors introduce new security challenges to the users, and make sensor management more complicated. In this PhD thesis, we contribute to the field of mobile sensor security by investigating a wide spectrum of open problems in this field covering attacks and defences, standardisation and industrial approaches, and human dimensions. We study the problems in detail and propose solutions. First, we propose “Tap-Tap and Pay” (TTP), a sensor-based protocol to prevent the Mafia attack in NFC payment. The Mafia attack is a special type of Man-In-The-Middle attack which charges the user for something more expensive than what she intends to pay by relaying transactions to a remote payment terminal. In TTP, a user initiates the payment by physically tapping her mobile phone against the reader. We observe that this tapping causes transient vibrations at both devices which are measurable by the embedded accelerometers. Our observations indicate that these sensor measurements are closely correlated within the same tapping, and different if obtained from different tapping events. By comparing the similarity between the two measurements, the bank can distinguish the Mafia fraud apart from a legitimate NFC transaction. The experimental results and the user feedback suggest the practical feasibility of TTP. As compared with previous sensor-based solutions, ours is the only one that works even when the attacker and the user are in nearby locations or share similar ambient environments. Second, we demonstrate an in-app attack based on a real world problem in contactless payment known as the card collision or card clash. A card collision happens when more than one card (or NFC-enabled device) are presented to the payment terminal’s field, and the terminal does not know which card to choose. By performing experiments, we observe that the implementation of contactless terminals in practice matches neither EMV nor ISO standards (the two primary standards for smart card payment) on card collision. Based on this inconsistency, we propose “NFC Payment Spy”, a malicious app that tracks the user’s contactless payment transactions. This app, running on a smart phone, simulates a card which requests the payment information (amount, time, etc.) from the terminal. When the phone and the card are both presented to a contactless terminal (given that many people use mobile case wallets to travel light and keep wallet essentials close to hand), our app can effectively win the race condition over the card. This attack is the first privacy attack on contactless payments based on the problem of card collision. By showing the feasibility of this attack, we raise awareness of privacy and security issues in contactless payment protocols and implementation, specifically in the presence of new technologies for payment such as mobile platforms. Third, we show that, apart from attacking mobile devices by having access to the sensors through native apps, we can also perform sensor-based attacks via mobile browsers. We examine multiple browsers on Android and iOS platforms and study their policies in granting permissions to JavaScript code with respect to access to motion and orientation sensor data. Based on our observations, we identify multiple vulnerabilities, and propose “TouchSignatures” and “PINLogger.js”, two novel attacks in which malicious JavaScript code listens to such sensor data measurements. We demonstrate that, despite the much lower sampling rate (comparing to a native app), a remote attacker is able to learn sensitive user information such as physical activities, phone call timing, touch actions (tap, scroll, hold, zoom), and PINs based on these sensor data. This is the first report of such a JavaScript-based attack. We disclosed the above vulnerability to the community and major mobile browser vendors classified the problem as high-risk and fixed it accordingly. Finally, we investigate human dimensions in the problem of sensor management. Although different types of attacks via sensors have been known for many years, the problem of data leakage caused by sensors has remained unsolved. While working with W3C and browser vendors to fix the identified problem, we came to appreciate the complexity of this problem in practice and the challenge of balancing security, usability, and functionality. We believe a major reason for this is that users are not fully aware of these sensors and the associated risks to their privacy and security. Therefore, we study user understanding of mobile sensors, specifically their risk perceptions. This is the only research to date that studies risk perceptions for a comprehensive list of mobile sensors (25 in total). We interview multiple participants from a range of backgrounds by providing them with multiple self-declared questionnaires. The results indicate that people in general do not have a good understanding of the complexities of these sensors; hence making security judgements about these sensors is not easy for them. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective. This makes the security and privacy issues of mobile sensors and other sensorenabled technologies an important topic to be investigated further

Looking towards the future: the changing nature of intrusive surveillance and technical attacks against high-profile targets

In this thesis a novel Bayesian model is developed that is capable of predicting the probability of a range of eavesdropping techniques deployed, given an attacker's capability, opportunity and intent. Whilst limited attention by academia has focused on the cold war activities of Soviet bloc and Western allies' bugging of embassies, even less attention has been paid to the changing nature of the technology used for these eavesdropping events. This thesis makes four contributions: through the analysis of technical eavesdropping events over the last century, technological innovation is shown to have enriched the eavesdropping opportunities for a range of capabilities. The entry barrier for effective eavesdropping is lowered, while for the well resourced eavesdropper, the requirement for close access has been replaced by remote access opportunities. A new way to consider eavesdropping methods is presented through the expert elicitation of capability and opportunity requirements for a range of present-day eavesdropping techniques. Eavesdropping technology is shown to have life-cycle stages with the technology exploited by different capabilities at different times. Three case studies illustrate that yesterday’s secretive government method becomes today’s commodity. The significance of the egress transmission path is considered too. Finally, by using the expert elicitation information derived for capability, opportunity and life-cycle position, for a range of eavesdropping techniques, it is shown that it is possible to predict the probability of particular eavesdropping techniques being deployed. This novel Bayesian inferencing model enables scenarios with incomplete, uncertain or missing detail to be considered. The model is validated against the previously collated historic eavesdropping events. The development of this concept may be scaled with additional eavesdropping techniques to form the basis of a tool for security professionals or risk managers wishing to define eavesdropping threat advice or create eavesdropping policies based on the rigour of this technological study.Open Acces