Biblio

Context-sensitive graph grammars have been rigorous formalisms for specifying visual programming languages, as they possess sufficient expressive powers and intuitive forms. Efficient parsing mechanisms are essential to these formalisms. However, the existent parsing algorithms are either inefficient or confined to a minority of graph grammars. This paper introduces the notion of partial precedence, defines the partial precedence graph of a graph grammar and theoretically unveils the existence of a valid parsing path conforming to the topological orderings of the partial precedence graph. Then, it provides algorithms for computing the partial precedence graph and presents an approach to improving general parsing algorithms with the graph based on the drawn conclusion. It is shown that the approach can considerably improve the efficiency of general parsing algorithms.

Matrix sketching is aimed at finding compact representations of a matrix while simultaneously preserving most of its properties, which is a fundamental building block in modern scientific computing. Randomized algorithms represent state-of-the-art and have attracted huge interest from the fields of machine learning, data mining, and theoretic computer science. However, it still requires the use of the entire input matrix in producing desired factorizations, which can be a major computational and memory bottleneck in truly large problems. In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an m-by-n matrix in only O(m+n) time and space. Indeed, the proposed method accesses only a small number of matrix rows and columns, which significantly improves the memory footprint. Meanwhile, by sequentially teaming two rounds of approximation procedures and upgrading the sampling strategy from a uniform probability to more sophisticated, encoding-orientated sampling, significant algorithmic boosting is achieved to uncover more granular structures in the data. Empirical results on a wide spectrum of real-world, large-scale matrices show that by taking only linear time and space, the accuracy of our method rivals those state-of-the-art randomized algorithms consuming a quadratic, O(mn), amount of resources.

Data-driven techniques are used in cyber-physical systems (CPS) for controlling autonomous vehicles, handling demand responses for energy management, and modeling human physiology for medical devices. These data-driven techniques extract models from training data, where their performance is often analyzed with respect to random errors in the training data. However, if the training data is maliciously altered by attackers, the effect of these attacks on the learning algorithms underpinning data-driven CPS have yet to be considered. In this paper, we analyze the resilience of classification algorithms to training data attacks. Specifically, a generic metric is proposed that is tailored to measure resilience of classification algorithms with respect to worst-case tampering of the training data. Using the metric, we show that traditional linear classification algorithms are resilient under restricted conditions. To overcome these limitations, we propose a linear classification algorithm with a majority constraint and prove that it is strictly more resilient than the traditional algorithms. Evaluations on both synthetic data and a real-world retrospective arrhythmia medical case-study show that the traditional algorithms are vulnerable to tampered training data, whereas the proposed algorithm is more resilient (as measured by worst-case tampering).

Many state-of-the-art information-flow control (IFC) tools are implemented as Haskell libraries. A distinctive feature of this language is lazy evaluation. In his influencal paper on why functional programming matters, John Hughes proclaims:,,Lazy evaluation is perhaps the most powerful tool for modularization in the functional programmer's repertoire.,,Unfortunately, lazy evaluation makes IFC libraries vulnerable to leaks via the internal timing covert channel. The problem arises due to sharing, the distinguishing feature of lazy evaluation, which ensures that results of evaluated terms are stored for subsequent re-utilization. In this sense, the evaluation of a term in a high context represents a side-effect that eludes the security mechanisms of the libraries. A naïve approach to prevent that consists in forcing the evaluation of terms before entering a high context. However, this is not always possible in lazy languages, where terms often denote infinite data structures. Instead, we propose a new language primitive, lazyDup, which duplicates terms lazily. By using lazyDup to duplicate terms manipulated in high contexts, we make the security library MAC robust against internal timing leaks via lazy evaluation. We show that well-typed programs satisfy progress-sensitive non-interference in our lazy calculus with non-strict references. Our security guarantees are supported by mechanized proofs in the Agda proof assistant.

Subliminal channels in digital signatures provide a very effective method to clandestinely leak information from inside a system to a third party outside. Information can be hidden in signature parameters in a way that both network operators and legitimate receivers would not notice any suspicious traces. Subliminal channels have previously been discovered in other signatures, such as ElGamal and ECDSA. Those signatures are usually just sparsely exchanged in network protocols, e.g. during authentication, and their usability for leaking information is therefore limited. With the advent of high-speed signatures such as EdDSA, however, scenarios become feasible where numerous packets with individual signatures are transferred between communicating parties. This significantly increases the bandwidth for transmitting subliminal information. Examples are broadcast clock synchronization or signed sensor data export. A subliminal channel in signatures appended to numerous packets allows the transmission of a high amount of hidden information, suitable for large scale data exfiltration or even the operation of command and control structures. In this paper, we show the existence of a broadband subliminal channel in the EdDSA signature scheme. We then discuss the implications of the subliminal channel in practice using thee different scenarios: broadcast clock synchronization, signed sensor data export, and classic TLS. We perform several experiments to show the use of the subliminal channel and measure the actual bandwidth of the subliminal information that can be leaked. We then discuss the applicability of different countermeasures against subliminal channels from other signature schemes to EdDSA but conclude that none of the existing solutions can sufficiently protect against data exfiltration in network protocols secured by EdDSA.

Cyber-physical systems have shown to be susceptible to cyber-attacks. Incidents such as Stuxnet Attack and Ukraine power outage have shown that attackers are capable of penetrating into industrial control systems, compromising PLCs, and sending false commands to physical devices while reporting normal sensing values. Therefore, one of the critical needs of CPS is to ensure the validity of the sensor values. In this paper, we explore path diversity in SCADA networks and develop Path Redundancy to improve data validity. The proposed solution is shown to be able to effectively prevent data integrity attacks and detect false command attacks from a single compromised path or PLC. We provide detailed analysis on solution design and implement an application of the technique in building automation networks. Our cost-efficient and easy-to-deploy solution improves the resilience of SCADA networks.

Combining Error-Correction Coding ECC and cryptography was proposed in the recent decade making use of bit-quality parameters to improve the error correction capability. Most of such techniques combine authentication crypto-functions jointly with ECC codes to improve system reliability, while fewer proposals involve ciphering functions with ECC to improve reliability. In this work, we propose practical and pragmatic low-cost approaches for making use of existing ciphering functions for reliability improvement. The presented techniques show that ciphering functions (as deterministic, non-linear bijective functions) can serve to achieve error correction enhancement and hence allow error recovery and scalable security trade-offs with or without additional ECC components. We demonstrate two best-effort error-correcting strategies. It is further shown, that the targeted reliability improvement is scalable to attain practical usability. The first proposed technique is pure-cipher-based error correction procedure deploying hard decision, best-effort operations to improve the system-survivability without changing system configuration. The second strategy is making use of ECC in combination with the ciphering function to enhance system-survivability. The correction procedures are based on simple experimental search-and-modify the corrupted ciphertext until predefined criteria become valid. This procedure may, however, turn out to become equivalent to a successful integrity/authenticity attack that may reduce the system security level, however in a scalable and predictable non-significant fashion.

With the development of smart grid, information and energy integrate deeply. For remote monitoring and cluster management, SCADA system of wind farm should be connected to Internet. However, communication security and operation risk put forward a challenge to data network of the wind farm. To address this problem, an active security defense strategy combined whitelist and security situation assessment is proposed. Firstly, the whitelist is designed by analyzing the legitimate packet of Modbus on communication of SCADA servers and PLCs. Then Knowledge Automation is applied to establish the Decision Requirements Diagram (DRD) for wind farm security. The D-S evidence theory is adopted to assess operation situation of wind farm and it together with whitelist offer the security decision for wind turbine. This strategy helps to eliminate the wind farm owners' security concerns of data networking, and improves the integrity of the cyber security defense for wind farm.

In this paper, we propose a novel adaptive control architecture for addressing security and safety in cyber-physical systems subject to exogenous disturbances. Specifically, we develop an adaptive controller for time-invariant, state-dependent adversarial sensor and actuator attacks in the face of stochastic exogenous disturbances. We show that the proposed controller guarantees uniform ultimate boundedness of the closed-loop dynamical system in a mean-square sense. We further discuss the practicality of the proposed approach and provide a numerical example involving the lateral directional dynamics of an aircraft to illustrate the efficacy of the proposed adaptive control architecture.

In this paper, cyber physical system is analyzed from security perspective. A double closed-loop security control structure and algorithm with defense functions is proposed. From this structure, the features of several cyber attacks are considered respectively. By this structure, the models of information disclosure, denial-of-service (DoS) and Man-in-the-Middle Attack (MITM) are proposed. According to each kind attack, different models are obtained and analyzed, then reduce to the unified models. Based on this, system security conditions are obtained, and a defense scenario with detail algorithm is design to illustrate the implementation of this program.

Guidelines, directives, and policy statements are usually presented in ``linear'' text form - word after word, page after page. However necessary, this practice impedes full understanding, obscures feedback dynamics, hides mutual dependencies and cascading effects and the like, - even when augmented with tables and diagrams. The net result is often a checklist response as an end in itself. All this creates barriers to intended realization of guidelines and undermines potential effectiveness. We present a solution strategy using text as ``data'', transforming text into a structured model, and generate a network views of the text(s), that we then can use for vulnerability mapping, risk assessments and control point analysis. We apply this approach using two NIST reports on cybersecurity of smart grid, more than 600 pages of text. Here we provide a synopsis of approach, methods, and tools. (Elsewhere we consider (a) system-wide level, (b) aviation e-landscape, (c) electric vehicles, and (d) SCADA for smart grid).

Cyber-physical systems are found in industrial and production systems, as well as critical infrastructures. Due to the increasing integration of IP-based technology and standard computing devices, the threat of cyber-attacks on cyber-physical systems has vastly increased. Furthermore, traditional intrusion defense strategies for IT systems are often not applicable in operational environments. In this paper we present an anomaly-based approach for detection and classification of attacks in cyber-physical systems. To test our approach, we set up a test environment with sensors, actuators and controllers widely used in industry, thus, providing system data as close as possible to reality. First, anomaly detection is used to define a model of normal system behavior by calculating outlier scores from normal system operations. This valid behavior model is then compared with new data in order to detect anomalies. Further, we trained an attack model, based on supervised attacks against the test setup, using the naive Bayes classifier. If an anomaly is detected, the classification process tries to classify the anomaly by applying the attack model and calculating prediction confidences for trained classes. To evaluate the statistical performance of our approach, we tested the model by applying an unlabeled dataset, which contains valid and anomalous data. The results show that this approach was able to detect and classify such attacks with satisfactory accuracy.

Due to the growing performance requirements, embedded systems are increasingly more complex. Meanwhile, they are also expected to be reliable. Guaranteeing reliability on complex systems is very challenging. Consequently, there is a substantial need for designs that enable the use of unverified components such as real-time operating system (RTOS) without requiring their correctness to guarantee safety. In this work, we propose a novel approach to design a controller that enables the system to restart and remain safe during and after the restart. Complementing this controller with a switching logic allows the system to use complex, unverified controller to drive the system as long as it does not jeopardize safety. Such a design also tolerates faults that occur in the underlying software layers such as RTOS and middleware and recovers from them through system-level restarts that reinitialize the software (middleware, RTOS, and applications) from a read-only storage. Our approach is implementable using one commercial off-the-shelf (COTS) processing unit. To demonstrate the efficacy of our solution, we fully implement a controller for a 3 degree of freedom (3DOF) helicopter. We test the system by injecting various types of faults into the applications and RTOS and verify that the system remains safe.

This paper1 introduces the notion of attribute-based concurrent signatures. This primitive can be considered as an interesting extension of concurrent signatures in the attribute-based setting. It allows two parties fairly exchange their signatures only if each of them has convinced the opposite party possesses certain attributes satisfying a given signing policy. Due to this new feature, this primitive can find useful applications in online contract signing, electronic transactions and so on. We formalize this notion and present a construction which is secure in the random oracle model under the Strong Diffie-Hellman assumption and the eXternal Diffie-Hellman assumption.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Wireless communications in Cyber-Physical Systems (CPS) are vulnerable to many adversarial attacks such as eavesdropping. To secure the communications, secret session keys need to be established between wireless devices. In existing symmetric key establishment protocols, it is assumed that devices are pre-loaded with secrets. In the CPS, however, wireless devices are produced by different companies. It is not practical to assume that the devices are pre-loaded with certain secrets when they leave companies. As a consequence, existing symmetric key establishment protocols cannot be directly implemented in the CPS. Motivated by these observations, this paper presents a cross-layer key establishment model for heterogeneous wireless devices in the CPS. Specifically, by implementing our model, wireless devices extract master keys (shared with the system authority) at the physical layer using ambient wireless signals. Then, the system authority distributes secrets for devices (according to an existing symmetric key establishment protocol) by making use of the extracted master keys. Completing these operations, wireless devices can establish secret session keys at higher layers by calling the employed key establishment protocol. Additionally, we prove the security of the proposed model. We analyse the performance of the new model by implementing it and converting existing symmetric key establishment protocols into cross-layer key establishment protocols.

This brief paper reports on an early stage ongoing PhD project in the field of cyber-physical security in health care critical infrastructures. The research overall aims to develop a methodology that will increase the ability of secure recovery of health critical infrastructures. This ambitious or reckless attempt, as it is currently at an early stage, in this paper, tries to answer why cyber-physical security for health care infrastructures is important and of scientific interest. An initial PhD project methodology and expected outcomes are also discussed. The report concludes with challenges that emerge and possible future directions.

In a software system it is possible to quantify the amount of information that is leaked or corrupted by analysing the flows of information present in the source code. In a cyber-physical system, information flows are not only present at the digital level but also at a physical level, and they are also present to and fro the two levels. In this work, we provide a methodology to formally analyse a composite, cyber-physical system model (combining physics and control) using an information flow-theoretic approach. We use this approach to quantify the level of vulnerability of a system with respect to attackers with different capabilities. We illustrate our approach by means of a water distribution case study.

Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquist-rate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense noises, this paper studies the compressive detection problem when the measurements are corrupted by both dense noises and sparse errors. The sparse errors exist in many practical systems, such as the ones affected by impulse noise or narrowband interference. We derive the theoretical performance of compressive detection when the sparse error is either deterministic or random. The theoretical results are further verified by simulations.

With the rapid and radical evolution of information and communication technology, energy consumption for wireless communication is growing at a staggering rate, especially for wireless multimedia communication. Recently, reducing energy consumption in wireless multimedia communication has attracted increasing attention. In this paper, we propose an energy-efficient wireless image transmission scheme based on adaptive block compressive sensing (ABCS) and SoftCast, which is called ABCS-SoftCast. In ABCS-SoftCast, the compression distortion and transmission distortion are considered in a joint manner, and the energy-distortion model is formulated for each image block. Then, the sampling rate (SR) and power allocation factors of each image block are optimized simultaneously. Comparing with conventional SoftCast scheme, experimental results demonstrate that the energy consumption can be greatly reduced even when the receiving image qualities are approximately the same.

A lot of research in security of cyber physical systems focus on threat models where an attacker can spoof sensor readings by compromising the communication channel. A little focus is given to attacks on physical components. In this paper a method to detect potential attacks on physical components in a Cyber Physical System (CPS) is proposed. Physical attacks are detected through a comparison of noise pattern from sensor measurements to a reference noise pattern. If an adversary has physically modified or replaced a sensor, the proposed method issues an alert indicating that a sensor is probably compromised or is defective. A reference noise pattern is established from the sensor data using a deterministic model. This pattern is referred to as a fingerprint of the corresponding sensor. The fingerprint so derived is used as a reference to identify measured data during the operation of a CPS. Extensive experimentation with ultrasonic level sensors in a realistic water treatment testbed point to the effectiveness of the proposed fingerprinting method in detecting physical attacks.

We show high confinement thermally tunable, low loss (0.27 ± 0.04 dB/cm) Si3N4waveguides that are 42 cm long. We show that this platform can enable the miniaturization of traditionally bulky active OCT components.

The high mobility of Army tactical networks, combined with their close proximity to hostile actors, elevates the risks associated with short-range network attacks. The connectivity model for such short range connections under active operations is extremely fluid, and highly dependent upon the physical space within which the element is operating, as well as the patterns of movement within that space. To handle these dependencies, we introduce the notion of "key cyber-physical terrain": locations within an area of operations that allow for effective control over the spread of proximity-dependent malware in a mobile tactical network, even as the elements of that network are in constant motion with an unpredictable pattern of node-to-node connectivity. We provide an analysis of movement models and approximation strategies for finding such critical nodes, and demonstrate via simulation that we can identify such key cyber-physical terrain quickly and effectively.

A semi-analytical model for internal optical losses at high power in a 1.5 μm laser diode with strong n-doping in the n-side of the optical confinement layer is created. The model includes intervalence band absorption by holes supplied by both current flow and two-photon absorption. The resulting losses are shown to be substantially lower than those in a similar, but weakly doped structure. Thus a significant improvement in the output power and efficiency by strong n-doping is predicted.