Biblio

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.

Improvements in mobile networking combined with the ubiquitous availability and adoption of low-cost development boards have enabled the vision of mobile platforms of Cyber-Physical Systems (CPS), such as fractionated spacecraft and UAV swarms. Computation and communication resources, sensors, and actuators that are shared among different applications characterize these systems. The cyber-physical nature of these systems means that physical environments can affect both the resource availability and software applications that depend on resource availability. While many application development and management challenges associated with such systems have been described in existing literature, resilient operation and execution have received less attention. This paper describes our work on improving runtime support for resilience in mobile CPS, with a special focus on our runtime infrastructure that provides autonomous resilience via self-reconfiguration. We also describe the interplay between this runtime infrastructure and our design-time tools, as the later is used to statically determine the resilience properties of the former. Finally, we present a use case study to demonstrate and evaluate our design-time resilience analysis and runtime self-reconfiguration infrastructure.

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, and naive sampling solutions are not suited for high-dimensional models. We propose ActiveClean, a progressive framework for training Machine Learning models with data cleaning. Our framework updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. We designed a visual interface to wrap around this framework and demonstrate ActiveClean for a video classification problem and a topic modeling problem.

In a world where highly skilled actors involved in cyber-attacks are constantly increasing and where the associated underground market continues to expand, organizations should adapt their defence strategy and improve consequently their security incident management. In this paper, we give an overview of Advanced Persistent Threats (APT) attacks life cycle as defined by security experts. We introduce our own compiled life cycle model guided by attackers objectives instead of their actions. Challenges and opportunities related to the specific camouflage actions performed at the end of each APT phase of the model are highlighted. We also give an overview of new APT protection technologies and discuss their effectiveness at each one of life cycle phases.

Differential privacy is a promising formal approach to data privacy, which provides a quantitative bound on the privacy cost of an algorithm that operates on sensitive information. Several tools have been developed for the formal verification of differentially private algorithms, including program logics and type systems. However, these tools do not capture fundamental techniques that have emerged in recent years, and cannot be used for reasoning about cutting-edge differentially private algorithms. Existing techniques fail to handle three broad classes of algorithms: 1) algorithms where privacy depends on accuracy guarantees, 2) algorithms that are analyzed with the advanced composition theorem, which shows slower growth in the privacy cost, 3) algorithms that interactively accept adaptive inputs. We address these limitations with a new formalism extending apRHL, a relational program logic that has been used for proving differential privacy of non-interactive algorithms, and incorporating aHL, a (non-relational) program logic for accuracy properties. We illustrate our approach through a single running example, which exemplifies the three classes of algorithms and explores new variants of the Sparse Vector technique, a well-studied algorithm from the privacy literature. We implement our logic in EasyCrypt, and formally verify privacy. We also introduce a novel coupling technique called optimal subset coupling that may be of independent interest.

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

Embodied conversational agents are changing the way humans interact with technology. In order to develop humanlike ECAs they need to be able to perform natural gestures that are used in day-to-day conversation. Gestures can give insight into an ECAs personality trait of extraversion, but what factors into it is still being explored. Our study focuses on two aspects of gesture: amplitude and frequency. Our goal is to find out whether agents should use specific gestures more frequently than others depending on the personality type they have been designed with. We also look to quantify gesture amplitude and compare it to a previous study on the perception of an agent's naturalness of its gestures. Our results showed some indication that introverts and extraverts judge the agent's naturalness similarly. The larger the amplitude our agent used, the more natural its gestures were perceived. The frequency of gestures between extraverts and introverts seem to contain hardly any difference, even in terms of types of gesture used.

This paper describes the challenges of converting the classic Pac-Man arcade game into a virtual reality game. Arcaid provides players with the tools to maintain sufficient situation awareness in an environment where, unlike the classic game, they do not have full view of the game state. We also illustrate methods that can be used to reduce a player's simulation sickness by providing visual focal points for players and designing user interface elements that do not disrupt immersion.

We present a demonstration of the ARIA framework, a modular approach for rapid development of virtual humans for information retrieval that have linguistic, emotional, and social skills and a strong personality. We demonstrate the framework's capabilities in a scenario where `Alice in Wonderland', a popular English literature book, is embodied by a virtual human representing Alice. The user can engage in an information exchange dialogue, where Alice acts as the expert on the book, and the user as an interested novice. Besides speech recognition, sophisticated audio-visual behaviour analysis is used to inform the core agent dialogue module about the user's state and intentions, so that it can go beyond simple chat-bot dialogue. The behaviour generation module features a unique new capability of being able to deal gracefully with interruptions of the agent.

Software Defined Internet Exchange Points (SDXes) increase the flexibility of interdomain traffic delivery on the Internet. Yet, an SDX inherently requires multiple participants to have access to a single, shared physical switch, which creates the need for an authorization mechanism to mediate this access. In this paper, we introduce a logic and mechanism called FLANC (A Formal Logic for Authorizing Network Control), which authorizes each participant to control forwarding actions on a shared switch and also allows participants to delegate forwarding actions to other participants at the switch (e.g., a trusted third party). FLANC extends "says" and "speaks for" logic that have been previously designed for operating system objects to handle expressions involving network traffic flows. We describe FLANC, explain how participants can use it to express authorization policies for realistic interdomain routing settings, and demonstrate that it is efficient enough to operate in operational settings.

The relevance of identity data leaks on the Internet is more present than ever. Almost every month we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to automatic analysis of a vast majority of bigger and smaller leaks. Our contribution is the concept and a prototype implementation of a parser, composed of a syntactic and a semantic module, and a data analyzer for identity leaks. In this context, we deal with the two major challenges of a huge amount of different formats and the recognition of leaks' unknown data types. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed.

The relevance of identity data leaks on the Internet is more present than ever. Almost every month we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to automatic analysis of a vast majority of bigger and smaller leaks. Our contribution is the concept and a prototype implementation of a parser, composed of a syntactic and a semantic module, and a data analyzer for identity leaks. In this context, we deal with the two major challenges of a huge amount of different formats and the recognition of leaks' unknown data types. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed.

Information Fusion (IF) systems have long exploited data provided by hard (physics-based) sensors with the aspiration of making sense of the environment they are monitoring. In recent times, the IF community has recognized the potential of utilizing data generated by people, also known as soft data. In this study, we demonstrate how course of action (CoA) generation, one of the key elements of Level 3 High-Level Information Fusion and a vital component for security and defense decision support systems, can be augmented using soft (human-derived) data for improved mission effectiveness. This conceptualization is validated through an elaborate experiment situated in the maritime world. To the best of the authors' knowledge, this is the first study to apply soft data to automatic CoA generation in the maritime domain.

Hardware-based enclave protection mechanisms, such as Intel’s SGX, ARM’s TrustZone, and Apple’s Secure Enclave, can protect code and data from powerful low-level attackers. In this work, we use enclaves to enforce strong application-specific information security policies. We present IMPE, a novel calculus that captures the essence of SGX-like enclave mechanisms, and show that a security-type system for IMPE can enforce expressive confidentiality policies (including erasure policies and delimited release policies) against powerful low-level attackers, including attackers that can arbitrarily corrupt non-enclave code, and, under some circumstances, corrupt enclave code. We present a translation from an expressive security-typed calculus (that is not aware of enclaves) to IMPE. The translation automatically places code and data into enclaves to enforce the security policies of the source program.

Software systems are often subject to unexpected runtime errors. Automatic runtime recovery (ARR) techniques aim at recovering them from erroneous states and maintaining them functional in the field. This paper proposes Ares , a novel, practical approach to performing ARR. Our key insight is to leverage a system's already built-in error handling support to recover from unexpected errors. To this end, we synthesize error handlers via two methods: error transformation and early return. We also equip Ares with a lightweight in-vivo testing infrastructure to select the right synthesis methods and avoid potentially dangerous error handlers. Unlike existing ARR techniques based on heavyweight mechanisms (e.g., checkpoint-restart and runtime monitoring), our approach expands the intrinsic capability of runtime error resilience already existing in software systems to handle unexpected errors. Ares's lightweight mechanism makes it practical and easy to be integrated into production environments. We have implemented Ares on top of both the Java HotSpot VM and Android ART, and applied it to 52 real-world bugs. The results are promising — Ares successfully recovers from 39 of them and incurs low overhead.

Cyber-physical systems (CPS) are often network integrated to enable remote management, monitoring, and reporting. Such integration has made them vulnerable to cyber attacks originating from an untrusted network (e.g., the internet). Once an attacker breaches the network security, he could corrupt operations of the system in question, which may in turn lead to catastrophes. Hence there is a critical need to detect intrusions into mission-critical CPS. Signature based detection may not work well for CPS, whose complexity may preclude any succinct signatures that we will need. Specification based detection requires accurate definitions of system behaviour that similarly can be hard to obtain, due to the CPS's complexity and dynamics, as well as inaccuracies and incompleteness of design documents or operation manuals. Formal models, to be tractable, are often oversimplified, in which case they will not support effective detection. In this paper, we study a behaviour-based machine learning (ML) approach for the intrusion detection. Whereas prior unsupervised ML methods have suffered from high missed detection or false-positive rates, we use a high-fidelity CPS testbed, which replicates all main physical and control components of a modern water treatment facility, to generate systematic training data for a supervised method. The method does not only detect the occurrence of a cyber attack at the physical process layer, but it also identifies the specific type of the attack. Its detection is fast and robust to noise. Furthermore, its adaptive system model can learn quickly to match dynamics of the CPS and its operating environment. It exhibits a low false positive (FP) rate, yet high precision and recall.

We propose a new voting scheme, BeleniosRF, that offers both receipt-freeness and end-to-end verifiability. It is receipt-free in a strong sense, meaning that even dishonest voters cannot prove how they voted. We provide a game-based definition of receipt-freeness for voting protocols with non-interactive ballot casting, which we name strong receipt-freeness (sRF). To our knowledge, sRF is the first game-based definition of receipt-freeness in the literature, and it has the merit of being particularly concise and simple. Built upon the Helios protocol, BeleniosRF inherits its simplicity and does not require any anti-coercion strategy from the voters. We implement BeleniosRF and show its feasibility on a number of platforms, including desktop computers and smartphones.

This paper addresses the problem of estimating the quality of an image as it would be perceived by a human. A well accepted approach to assess perceptual quality of an image is to quantify its loss of structural information. We propose a blind image quality assessment method that aims at quantifying structural information loss in a given (possibly distorted) image by comparing its structures with those extracted from a database of clean images. We first construct a subspace from the clean natural images using (i) principal component analysis (PCA), and (ii) overcomplete dictionary learning with sparsity constraint. While PCA provides mathematical convenience, an overcomplete dictionary is known to capture the perceptually important structures resembling the simple cells in the primary visual cortex. The subspace learned from the clean images is called the source subspace. Similarly, a subspace, called the target subspace, is learned from the distorted image. In order to quantify the structural information loss, we use a subspace alignment technique which transforms the target subspace into the source by optimizing over a transformation matrix. This transformation matrix is subsequently used to measure the global and local (patch-based) quality score of the distorted image. The quality scores obtained by the proposed method are shown to correlate well with the subjective scores obtained from human annotators. Our method achieves competitive results when evaluated on three benchmark databases.