Biblio

Impedance control is a common framework for control of lower-limb prosthetic devices. This approach requires choosing many impedance controller parameters. In this paper, we show how to learn these parameters for lower-limb prostheses by observation of unimpaired human walkers. We validate our approach in simulation of a transfemoral amputee, and we demonstrate the performance of the learned parameters in a preliminary experiment with a lower-limb prosthetic device.

When SCADA systems are exposed to public networks, attackers can more easily penetrate the control systems that operate electrical power grids, water plants, and other critical infrastructures. To detect such attacks, SCADA systems require an intrusion detection technique that can understand the information carried by their usually proprietary network protocols.

To achieve that goal, we propose to attach to SCADA systems a specification-based intrusion detection framework based on Bro [7][8], a runtime network traffic analyzer. We have built a parser in Bro to support DNP3, a network protocol widely used in SCADA systems that operate electrical power grids. This built-in parser provides a clear view of all network events related to SCADA systems. Consequently, security policies to analyze SCADA-specific semantics related to the network events can be accurately defined. As a proof of concept, we specify a protocol validation policy to verify that the semantics of the data extracted from network packets conform to protocol definitions. We performed an experimental evaluation to study the processing capabilities of the proposed intrusion detection framework.

In the current generation of SCADA (Supervisory Control And Data Acquisition) systems used in power grids, a sophisticated attacker can exploit system vulnerabilities and use a legitimate maliciously crafted command to cause a wide range of system changes that traditional contingency analysis does not consider and remedial action schemes cannot handle. To detect such malicious commands, we propose a semantic analysis framework based on a distributed network of intrusion detection systems (IDSes). The framework combines system knowledge of both cyber and physical infrastructure in power grid to help IDS to estimate execution consequences of control commands, thus to reveal attacker’s malicious intentions. We evaluated the approach on the IEEE 30-bus system. Our experiments demonstrate that: (i) by opening 3 transmission lines, an attacker can avoid detection by the traditional contingency analysis and instantly put the tested 30-bus system into an insecure state and (ii) the semantic analysis provides reliable detection of malicious commands with a small amount of analysis time.

Healthcare technology—sometimes called “healthtech” or “healthsec”—is enmeshed with security and privacy via usability, performance, and cost-effectiveness issues. It is multidisciplinary, distributed, and complex—and it also involves many competing stakeholders and interests. To address the problems that arise in such a multifaceted field—comprised of physicians, IT professionals, management information specialists, computer scientists, edical informaticists, and epidemiologists, to name a few—the Healthtech Declaration was initiated at the most recent USENIX Summit on Information Technologies for Health (Healthtech 2015) held in Washington, DC. This Healthtech Declaration includes an easy-touse—and easy-to-cite—checklist of key issues that anyone proposing a solution must consider (see “The Healthtech Declaration Checklist” sidebar). In this article, we provide the context and motivation for the declaration.

The prevalence of smart devices has promoted the popularity of mobile applications (a.k.a. apps) in recent years. A number of interesting and important questions remain unanswered, such as why a user likes/dislikes an app, how an app becomes popular or eventually perishes, how a user selects apps to install and interacts with them, how frequently an app is used and how much trac it generates, etc. This paper presents an empirical analysis of app usage behaviors collected from millions of users of Wandoujia, a leading Android app marketplace in China. The dataset covers two types of user behaviors of using over 0.2 million Android apps, including (1) app management activities (i.e., installation, updating, and uninstallation) of over 0.8 million unique users and (2) app network trac from over 2 million unique users. We explore multiple aspects of such behavior data and present interesting patterns of app usage. The results provide many useful implications to the developers, users, and disseminators of mobile apps.

Today's cyber-physical systems (CPSs) can have very different characteristics in terms of control algorithms, configurations, underlying infrastructure, communication protocols, and real-time requirements. Despite these variations, they all face the threat of malicious attacks that exploit the vulnerabilities in the cyber domain as footholds to introduce safety violations in the physical processes. In this paper, we focus on a class of attacks that impact the physical processes without introducing anomalies in the cyber domain. We present the common challenges in detecting this type of attacks in the contexts of two very different CPSs (i.e., power grids and surgical robots). In addition, we present a general principle for detecting such cyber-physical attacks, which combine the knowledge of both cyber and physical domains to estimate the adverse consequences of malicious activities in a timely manner.

A thorough understanding of society’s privacy incidents is of paramount importance for technical solutions, training/education, social research, and legal scholarship in privacy. The goal of the PrIncipedia project is to provide this understanding by developing the first comprehensive database of privacy incidents, enabling the exploration of a variety of privacy-related research questions. We provide a working definition of “privacy incident” and evidence that it meets end-user perceptions of privacy. We also provide semi-automated support for building the database through a learned classifier that detects news articles about privacy incidents.

In a multiagent system, a (social) norm describes what the agents may expect from each other.  Norms promote autonomy (an agent need not comply with a norm) and heterogeneity (a norm describes interactions at a high level independent of implementation details). Researchers have studied norm emergence through social learning where the agents interact repeatedly in a graph structure.

In contrast, we consider norm emergence in an open system, where membership can change, and where no predetermined graph structure exists.  We propose Silk, a mechanism wherein a generator monitors interactions among member agents and recommends norms to help resolve conflicts.  Each member decides on whether to accept or reject a recommended norm.  Upon exiting the system, a member passes its experience along to incoming members of the same type.  Thus, members develop norms in a hybrid manner to resolve conflicts.

We evaluate Silk via simulation in the traffic domain.  Our results show that social norms promoting conflict resolution emerge in both moderate and selfish societies via our hybrid mechanism.

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices.  Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Intrusion detection systems (IDSs) assume increasingly importance in past decades as information systems become ubiquitous. Despite the abundance of intrusion detection algorithms developed so far, there is still no single detection algorithm or procedure that can catch all possible intrusions; also, simultaneously running all these algorithms may not be feasible for practical IDSs due to resource limitation. For these reasons, effective IDS configuration becomes crucial for real-time intrusion detection. However, the uncertainty in the intruder’s type and the (often unknown) dynamics involved with the target system pose challenges to IDS configuration. Considering these challenges, the IDS configuration problem is formulated as an incomplete information stochastic game in this work, and a new algorithm, Bayesian Nash-Q learning, that combines conventional reinforcement learning with a Bayesian type identification procedure is proposed. Numerical results show that the proposed algorithm can identify the intruder’s type with high fidelity and provide effective configuration.

The quantity of personal data gathered by service providers via our daily activities continues to grow at a rapid pace. The sharing, and the subsequent analysis of, such data can support a wide range of activities, but concerns around privacy often prompt an organization to transform the data to meet certain protection models (e.g., k-anonymity or E-differential privacy). These models, however, are based on simplistic adversarial frameworks, which can lead to both under- and over-protection. For instance, such models often assume that an adversary attacks a protected record exactly once. We introduce a principled approach to explicitly model the attack process as a series of steps. Specically, we engineer a factored Markov decision process (FMDP) to optimally plan an attack from the adversary's perspective and assess the privacy risk accordingly. The FMDP captures the uncertainty in the adversary's belief (e.g., the number of identied individuals that match the de-identified data) and enables the analysis of various real world deterrence mechanisms beyond a traditional protection model, such as a penalty for committing an attack. We present an algorithm to solve the FMDP and illustrate its efficiency by simulating an attack on publicly accessible U.S. census records against a real identied resource of over 500,000 individuals in a voter registry. Our results demonstrate that while traditional privacy models commonly expect an adversary to attack exactly once per record, an optimal attack in our model may involve exploiting none, one, or more indiviuals in the pool of candidates, depending on context.

Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.

A frequent claim that has not been validated is that signature based network intrusion detection systems (SNIDS) cannot detect zero-day attacks. This paper studies this property by testing 356 severe attacks on the SNIDS Snort, configured with an old official rule set. Of these attacks, 183 attacks are zero-days' to the rule set and 173 attacks are theoretically known to it. The results from the study show that Snort clearly is able to detect zero-days' (a mean of 17% detection). The detection rate is however on overall greater for theoretically known attacks (a mean of 54% detection). The paper then investigates how the zero-days' are detected, how prone the corresponding signatures are to false alarms, and how easily they can be evaded. Analyses of these aspects suggest that a conservative estimate on zero-day detection by Snort is 8.2%.

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.
 

Decreasing the potential for catastrophic consequences poses a significant challenge for high-risk industries. Organizations are under many different pressures, and they are continuously trying to adapt to changing conditions and recover from disturbances and stresses that can arise from both normal operations and unexpected events. Reducing risks in complex systems therefore requires that organizations develop and enhance traits that increase resilience. Resilience provides a holistic approach to safety, emphasizing the creation of organizations and systems that are proactive, interactive, reactive, and adaptive. This approach relies on disciplines such as system safety and emergency management, but also requires that organizations develop indicators and ways of knowing when an emergency is imminent. A resilient organization must be adaptive, using hands-on activities and lessons learned efforts to better prepare it to respond to future disruptions. It is evident from the discussions of each of the traits of resilience, including their limitations, that there are no easy answers to reducing safety risks in complex systems. However, efforts to strengthen resilience may help organizations better address the challenges associated with the ever-increasing complexities of their systems.

As the interconnect delay is becoming a larger fraction of the clock cycle time, the conventional global stalling mechanism, which is used to correct error in general synchronous circuits, would be no longer feasible because of the expensive timing cost for the stalling signal to travel across the circuit. In this paper, we propose recovery-based resilient latency-insensitive systems (RLISs) that efficiently integrate error-recovery techniques with latency-insensitive design to replace the global stalling. We first demonstrate a baseline RLIS as the motivation of our work that uses additional output buffer which guarantees that only correct data can enter the output channel. However this baseline RLIS suffers from performance degradations even when errors do not occur. We propose a novel improved RLIS that allows erroneous data to propagate in the system. Equipped with improved queues that prevent accumulation of erroneous data, the improved RLIS retains the system performance. We provide theoretical study that analyzes the impact of errors on system performance and the queue sizing problem. We also theoretically prove that the improved RLIS performs no worse than the global stalling mechanism. Experimental results show that the improved RLIS has 40.3% and even 3.1% throughput improvements compared to the baseline RLIS and the infeasible global stalling mechanism respectively, with less than 10% hardware overhead.
 

Checking remote data possession is of crucial importance in public cloud storage. It enables the users to check whether their outsourced data have been kept intact without downloading the original data. The existing remote data possession checking (RDPC) protocols have been designed in the PKI (public key infrastructure) setting. The cloud server has to validate the users' certificates before storing the data uploaded by the users in order to prevent spam. This incurs considerable costs since numerous users may frequently upload data to the cloud server. This study addresses this problem with a new model of identity-based RDPC (ID-RDPC) protocols. The authors present the first ID-RDPC protocol proven to be secure assuming the hardness of the standard computational Diffie-Hellman problem. In addition to the structural advantage of elimination of certificate management and verification, the authors ID-RDPC protocol also outperforms the existing RDPC protocols in the PKI setting in terms of computation and communication.
 

We propose Authentication and Key Agreement (AKA) for Machine Type Communications (MTC) in LTE-Advanced. This protocol is based on an idea of grouping devices so that it would reduce signaling congestion in the access network and overload on the single authentication server. We verified that this protocol is designed to be secure against many attacks by using a software verification tool. Furthermore, performance evaluation suggests that this protocol is efficient with respect to authentication overhead and handover delay.