Biblio

The analysis of real-world protocols, in particular key exchange protocols and protocols building on these protocols, is a very complex, error-prone, and tedious task. Besides the complexity of the protocols itself, one important reason for this is that the security of the protocols has to be reduced to the security of the underlying cryptographic primitives for every protocol time and again. We would therefore like to get rid of reduction proofs for real-world key exchange protocols as much as possible and in many cases altogether, also for higher-level protocols which use the exchanged keys. So far some first steps have been taken in this direction. But existing work is still quite limited, and, for example, does not support Diffie-Hellman (DH) key exchange, a prevalent cryptographic primitive for real-world protocols. In this paper, building on work by Kusters and Tuengerthal, we provide an ideal functionality in the universal composability setting which supports several common cryptographic primitives, including DH key exchange. This functionality helps to avoid reduction proofs in the analysis of real-world protocols and often eliminates them completely. We also propose a new general ideal key exchange functionality which allows higherlevel protocols to use exchanged keys in an ideal way. As a proof of concept, we apply our framework to three practical DH key exchange protocols, namely ISO 9798-3, SIGMA, and OPTLS.

The security of almost any real-world distributed system today depends on the participants having some "reasonably accurate" sense of current real time. Indeed, to name one example, the very authenticity of practically any communication on the Internet today hinges on the ability of the parties to accurately detect revocation of certificates, or expiration of passwords or shared keys.,,However, as recent attacks show, the standard protocols for determining time are subvertible, resulting in wide-spread security loss. Worse yet, we do not have security notions for network time protocols that (a) can be rigorously asserted, and (b) rigorously guarantee security of applications that require a sense of real time.,,We propose such notions, within the universally composable (UC) security framework. That is, we formulate ideal functionalities that capture a number of prevalent forms of time measurement within existing systems. We show how they can be realized by real-world protocols, and how they can be used to assert security of time-reliant applications - specifically, certificates with revocation and expiration times. This allows for relatively clear and modular treatment of the use of time consensus in security-sensitive systems.,,Our modeling and analysis are done within the existing UC framework, in spite of its asynchronous, event-driven nature. This allows incorporating the use of real time within the existing body of analytical work done in this framework. In particular it allows for rigorous incorporation of real time within cryptographic tools and primitives.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,łdots, Ym, where Y$\mathscrl$ is independently $ε\mathscrl$-Bayesian differential private for $\mathscrl$ = 1,2,łdots, m, then by feeding the result of Y1 into Y2, the result of Y2 into Y3, and so on, we will finally have an $Σ$m$\mathscrl$=;1 $ε\mathscrl$-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The $\mathscrl$-th subset is input to a Bayesian differential private algorithm Y$\mathscrl$, for $\mathscrl$= 1, 2,łdots, m. Then the parallel composition of Y1, Y2,łdots, Ym will be maxm$\mathscrl$=;1=1 $ε\mathscrl$-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge abo- t the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.

Despite the advent of numerous Internet-of-Things (IoT) applications, recent research demonstrates potential side-channel vulnerabilities exploiting sensors which are used for event and environment monitoring. In this paper, we propose a new side-channel attack, where a network of distributed non-acoustic sensors can be exploited by an attacker to launch an eavesdropping attack by reconstructing intelligible speech signals. Specifically, we present PitchIn to demonstrate the feasibility of speech reconstruction from non-acoustic sensor data collected offline across networked devices. Unlike speech reconstruction which requires a high sampling frequency (e.g., textgreater 5 KHz), typical applications using non-acoustic sensors do not rely on richly sampled data, presenting a challenge to the speech reconstruction attack. Hence, PitchIn leverages a distributed form of Time Interleaved Analog-Digital-Conversion (TIADC) to approximate a high sampling frequency, while maintaining low per-node sampling frequency. We demonstrate how distributed TI-ADC can be used to achieve intelligibility by processing an interleaved signal composed of different sensors across networked devices. We implement PitchIn and evaluate reconstructed speech signal intelligibility via user studies. PitchIn has word recognition accuracy as high as 79%. Though some additional work is required to improve accuracy, our results suggest that eavesdropping using a fusion of non-acoustic sensors is a real and practical threat.

Due to the increasing complexity of design process, outsourcing, and use of third-party blocks, it becomes harder and harder to prevent Trojan insertion and other malicious design modifications. In this paper, we propose to deploy security invariant as carried proof to prevent and detect Trojans and malicious attacks and to ensure the security of hardware design. Non-interference with down-grading policy is checked for confidentiality. Contrary to existing approaches by type checking, we develop a method to model-check a simple safety property on a composed machine. Down-grading is handled in a better way in model-checking and the effectiveness of our approach is demonstrated on various Verilog benchmarks.

This paper introduces a cryptographic protocol for efficiently aggregating a count of unique items across a set of data parties privately - that is, without exposing any information other than the count. Our protocol allows for more secure and useful statistics gathering in privacy-preserving distributed systems such as anonymity networks; for example, it allows operators of anonymity networks such as Tor to securely answer the questions: how many unique users are using the distributed service? and how many hidden services are being accessed?. We formally prove the correctness and security of our protocol in the Universal Composability framework against an active adversary that compromises all but one of the aggregation parties. We also show that the protocol provides security against adaptive corruption of the data parties, which prevents them from being victims of targeted compromise. To ensure safe measurements, we also show how the output can satisfy differential privacy. We present a proof-of-concept implementation of the private set-union cardinality protocol (PSC) and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. In particular, for reasonable deployment sizes, the protocol run at timescales smaller than the typical measurement period would be and thus is suitable for distributed measurement.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as $łambda$JS and implement a runtime checked type extension using code instrumentation for full JavaScript.

Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost – violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.

We analyze the security practices of three smart toys that communicate with children through voice commands. We show the general communication architecture, and some general security and privacy practices by each of the devices. Then we focus on the analysis of one particular toy, and show how attackers can decrypt communications to and from a target device, and perhaps more worryingly, the attackers can also inject audio into the toy so the children listens to any arbitrary audio file the attacker sends to the toy. This last attack raises new safety concerns that manufacturers of smart toys should prevent.

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though "hidden", are nonetheless audible. In this work, we design a totally inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f textgreater 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validated DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions, and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the controller area network (CAN) bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper provides the first such algorithm with experiments using three attacks in five (total) scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).

Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries. Previous papers attack voice assistants with obfuscated voice commands by leveraging the gap between speech recognition system and human voice perception. The limitation is that these obfuscated commands are audible and thus conspicuous to device owners. In this poster, we propose a novel mechanism to directly attack the microphone used for sensing voice data with inaudible voice commands. We show that the adversary can exploit the microphone's non-linearity and play well-designed inaudible ultrasounds to cause the microphone to record normal voice commands, and thus control the victim device inconspicuously. We demonstrate via end-to-end real-world experiments that our inaudible voice commands can attack an Android phone and an Amazon Echo device with high success rates at a range of 2-3 meters.

Nowadays the Internet is closely related to our daily life. We enjoy the quality of service the provided by The Internet at the same time, but also suffer from the threat of network security. Among the many threats, SQL injection attacks are ranked in the first place. SQL injection attack refers to “when the user sends a request to the server, the malicious SQL command will be inserted into the web form or request URL parameters, leading to the server to perform illegal SQL query. The existing SQL injection detection methods include static analysis, dynamic analysis, parameterized query, intrusion detection system, parameter filtering and so on. However, these methods have some defects. Static analysis method can only detect the type and grammatical errors of SQL. Dynamic analysis can only detect the vulnerability predefined by application developers. Parameter filtering is based on regular expressions and black list to filter invalid characters. This method needs predefined regular expressions, but due to the diversity of SQL syntax and user input, resulting in a regular expression can't meet the requirements of detection, and has the defects that the attackers bypass detection to inject by the way of encoding parameters. In this paper, we propose a new approach to detect and prevent SQL injection. Our approach is based on the attack behavior and the analysis of response and state of the web application under different attacks. Our method perfectly solves the problems existing in methods mentioned above, and has higher accuracy.
 

Robotic vehicles and especially autonomous robotic vehicles can be attractive targets for attacks that cross the cyber-physical divide, that is cyber attacks or sensory channel attacks affecting the ability to navigate or complete a mission. Detection of such threats is typically limited to knowledge-based and vehicle-specific methods, which are applicable to only specific known attacks, or methods that require computation power that is prohibitive for resource-constrained vehicles. Here, we present a method based on Bayesian Networks that can not only tell whether an autonomous vehicle is under attack, but also whether the attack has originated from the cyber or the physical domain. We demonstrate the feasibility of the approach on an autonomous robotic vehicle built in accordance with the Generic Vehicle Architecture specification and equipped with a variety of popular communication and sensing technologies. The results of experiments involving command injection, rogue node and magnetic interference attacks show that the approach is promising.

Usually, the air gap will appear inside the composite insulators and it will lead to serious accident. In order to detect these internal defects in composite insulators operated in the transmission lines, a new non-destructive technique has been proposed. In the study, the mathematical analysis model of the composite insulators inner defects, which is about heat diffusion, has been build. The model helps to analyze the propagation process of heat loss and judge the structure and defects under the surface. Compared with traditional detection methods and other non-destructive techniques, the technique mentioned above has many advantages. In the study, air defects of composite insulators have been made artificially. Firstly, the artificially fabricated samples are tested by flash thermography, and this method shows a good performance to figure out the structure or defects under the surface. Compared the effect of different excitation between flash and hair drier, the artificially samples have a better performance after heating by flash. So the flash excitation is better. After testing by different pollution on the surface, it can be concluded that different pollution don't have much influence on figuring out the structure or defect under the surface, only have some influence on heat diffusion. Then the defective composite insulators from work site are detected and the image of defect is clear. This new active thermography system can be detected quickly, efficiently and accurately, ignoring the influence of different pollution and other environmental restrictions. So it will have a broad prospect of figuring out the defeats and structure in composite insulators even other styles of insulators.

Complex safety-critical devices require dependable communication. Dependability includes confidentiality and integrity as much as safety. Encrypting gateways with demilitarized zones, Multiple Independent Levels of Security architectures and the infamous Air Gap are diverse integration patterns for safety-critical infrastructure. Though resource restricted embedded safety devices still lack simple, certifiable, and efficient cryptography implementations. Following the recommended formal methods approach for safety-critical devices, we have implemented proven cryptography algorithms in the qualified model based language Scade as the Safety Leveraged Implementation of Data Encryption (SLIDE) library. Optimization for the synchronous dataflow language is discussed in the paper. The implementation for public-key based encryption and authentication is evaluated for real-world performance. The feasibility is shown by execution time benchmarks on an industrial safety microcontroller platform running a train control safety application.

The proliferation of Internet-of-Things (IoT) devices within homes raises many security and privacy concerns. Recent headlines highlight the lack of effective security mechanisms in IoT devices. Security threats in IoT arise not only from vulnerabilities in individual devices but also from the composition of devices in unanticipated ways and the ability of devices to interact through both cyber and physical channels. Existing approaches provide methods for monitoring cyber interactions between devices but fail to consider possible physical interactions. To overcome this challenge, it is essential that security assessments of IoT networks take a holistic view of the network and treat it as a "system of systems", in which security is defined, not solely by the individual systems, but also by the interactions and trust dependencies between systems. In this paper, we propose a way of modeling cyber and physical interactions between IoT devices of a given network. By verifying the cyber and physical interactions against user-defined policies, our model can identify unexpected chains of events that may be harmful. It can also be applied to determine the impact of the addition (or removal) of a device into an existing network with respect to dangerous device interactions. We demonstrate the viability of our approach by instantiating our model using Alloy, a language and tool for relational models. In our evaluation, we considered three realistic IoT use cases and demonstrate that our model is capable of identifying potentially dangerous device interactions. We also measure the performance of our approach with respect to the CPU runtime and memory consumption of the Alloy model finder, and show that it is acceptable for smart-home IoT networks.

Tracing and integrating security requirements throughout the development process is a key challenge in security engineering. In socio-technical systems, security requirements for the organizational and technical aspects of a system are currently dealt with separately, giving rise to substantial misconceptions and errors. In this paper, we present a model-based security engineering framework for supporting the system design on the organizational and technical level. The key idea is to allow the involved experts to specify security requirements in the languages they are familiar with: business analysts use BPMN for procedural system descriptions; system developers use UML to design and implement the system architecture. Security requirements are captured via the language extensions SecBPMN2 and UMLsec. We provide a model transformation to bridge the conceptual gap between SecBPMN2 and UMLsec. Using UMLsec policies, various security properties of the resulting architecture can be verified. In a case study featuring an air traffic management system, we show how our framework can be practically applied.

Many a time's assumptions are key to inventions. One such notion in recent past is about data exchange between two disjoint computer systems. It is always assumed that, if any two computers are separated physically without any inter communication, it is considered to be very secure and will not be compromised, the exchange of data between them would be impossible. But recent growth in the field of computers emphasizes the requirements of security analysis. One such security concern is with the air-gapped systems. This paper deals with the flaws and flow of air-gapped systems.

In Germany, as of 2017, a new smart metering infrastructure based on high security and privacy requirements will be deployed. It provides interfaces to connect meters for different commodities, to allow end users to retrieve the collected measurement data, to connect to the metering operators, and to connect Controllable Local Systems (CLSs) that establish a TLS secured connection to third parties in order to exchange data or for remote controlling of energy devices. This paper aims to connect industrial machines as CLS devices since it shows that the demands and main ideas of remotely controlled devices in the Smart Grid context and Industrial Cloud Applications match on the communication level. It describes the general architecture of the Smart Metering infrastructure in Germany, introduces the defined roles, depicts the configuration process on the different organizational levels, demonstrates the connection establishment and the initiating partners, concludes on the potential industrial use cases of this infrastructure, and provides open questions and room for further research.

In this paper, we introduce a secure energy trading auction approach to schedule the power plant limited resources during peak hours time slots. In the proposed auction model, the power plant serving a power grid shares with the smart meters its available amount of resources that is expected during the next future peak time slot; smart meters expecting a demand for additional power participate in the power auction by submitting bids of their offered price for their requested amount of power. In order to secure the power auction and protect smart meters' privacy, homomorphic encryption through Paillier cryptosystem is used to secure the bidding values and ensure avoiding possible insincere behaviors of smart meters or the grid operator (i.e. the auctioneer) to manipulate the auction for their own benefits. In addition, we use a payment rule that maximizes the power plant's revenue. We propose an efficient power scheduling mechanism to distribute the operator's limited resources among smart meters participating in the power auction. Finally, we present simulation results for the performance of our secure power scheduling auction mechanism.

This research in progress paper describes the role of cyber security measures undertaken in an ICT system for integrating electric storage technologies into the grid. To do so, it defines security requirements for a communications gateway and gives detailed information and hands-on configuration advice on node and communication line security, data storage, coping with backend M2M communications protocols and examines privacy issues. The presented research paves the road for developing secure smart energy communications devices that allow enhancing energy efficiency. The described measures are implemented in an actual gateway device within the HORIZON 2020 project STORY, which aims at developing new ways to use storage and demonstrating these on six different demonstration sites.

Protection of information achieves keeping confidentiality, integrity, and availability of the data. These features are essential for the proper operation of modern industrial technologies, like Smart Grid. The complex grid system integrates many electronic devices that provide an efficient way of exploiting the power systems but cause many problems due to their vulnerabilities to attacks. The aim of the work is to propose a solution to the privacy problem in Smart Grid communication network between the customers and Control center. It consists in using the relatively new cryptographic task - quantum key distribution (QKD). The solution is based on choosing an appropriate quantum key distribution method out of all the conventional ones by performing an assessment in terms of several parameters. The parameters are: key rate, operating distances, resources, and trustworthiness of the devices involved. Accordingly, we discuss an answer to the privacy problem of the SG network with regard to both security and resource economy.

This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.