Biblio

The anonymizing network Tor is examined as one method of anonymizing port scanning tools and avoiding identification and retaliation. Performing anonymized port scans through Tor is possible using Nmap, but parallelization of the scanning processes is required to accelerate the scan rate.

We present history-independent alternatives to a B-tree, the primary indexing data structure used in databases. A data structure is history independent (HI) if it is impossible to deduce any information by examining the bit representation of the data structure that is not already available through the API. We show how to build a history-independent cache-oblivious B-tree and a history-independent external-memory skip list. One of the main contributions is a data structure we build on the way–-a history-independent packed-memory array (PMA). The PMA supports efficient range queries, one of the most important operations for answering database queries. Our HI PMA matches the asymptotic bounds of prior non-HI packed-memory arrays and sparse tables. Specifically, a PMA maintains a dynamic set of elements in sorted order in a linear-sized array. Inserts and deletes take an amortized O(log2 N) element moves with high probability. Simple experiments with our implementation of HI PMAs corroborate our theoretical analysis. Comparisons to regular PMAs give preliminary indications that the practical cost of adding history-independence is not too large. Our HI cache-oblivious B-tree bounds match those of prior non-HI cache-oblivious B-trees. Searches take O(logB N) I/Os; inserts and deletes take O((log2 N)/B+ logB N) amortized I/Os with high probability; and range queries returning k elements take O(logB N + k/B) I/Os. Our HI external-memory skip list achieves optimal bounds with high probability, analogous to in-memory skip lists: O(logB N) I/Os for point queries and amortized O(logB N) I/Os for inserts/deletes. Range queries returning k elements run in O(logB N + k/B) I/Os. In contrast, the best possible high-probability bounds for inserting into the folklore B-skip list, which promotes elements with probability 1/B, is just Theta(log N) I/Os. This is no better than the bounds one gets from running an in-memory skip list in external memory.

We present security vulnerabilities in the remote voting system Helios. We propose Apollo, a modified version of Helios, which addresses these vulnerabilities and could improve the feasibility of internet voting.

In particular, we note that Apollo does not possess Helios' major known vulnerability, where a dishonest voting terminal can change the vote after it obtains the voter's credential. With Apollo-lite, votes not authorized by the voter are detected by the public and prevented from being included in the tally.

The full version of Apollo enables a voter to prove that her vote was changed. We also describe a very simple protocol for the voter to interact with any devices she employs to check on the voting system, to enable frequent and easy auditing of encryptions and checking of the bulletin board.
 

Conductor copies of musical scores are typically rich in handwritten annotations. Ongoing archival efforts to digitize orchestral conductors' scores have made scanned copies of hundreds of these annotated scores available in digital formats. The extraction of handwritten annotations from digitized printed documents is a difficult task for computer vision, with most approaches focusing on the extraction of handwritten text. However, conductors' annotation practices provide us with at least two affordances, which make the task more tractable in the musical domain. First, many conductors opt to mark their scores using colored pencils, which contrast with the black and white print of sheet music. Consequently, we show promising results when using color separation techniques alone to recover handwritten annotations from conductors' scores. We also compare annotated scores to unannotated copies and use a printed sheet music comparison tool to recover handwritten annotations as additions to the clean copy. We then investigate the use of both of these techniques in a combined method, which improves the results of the color separation technique. These techniques are demonstrated using a sample of orchestral scores annotated by professional conductors of the New York Philharmonic. Handwritten annotation extraction in musical scores has applications to the systematic investigation of score annotation practices by performers, annotator attribution, and to the interactive presentation of annotated scores, which we briefly discuss.

With the boom of ride-sharing platforms, there has been a growing debate on ride-sharing regulations. In particular, allegations of rape against ride-sharing drivers put sexual assault at the center of this debate. However, there is no systematic and society-wide evidence regarding ride-sharing and sexual assault. Building on a theory of crime victimization, this study examines the effect of ride-sharing on sexual assault incidents using comprehensive data on Uber transactions and crime incidents in New York City over the period from January to March 2015. Our findings demonstrate that the Uber availability is negatively associated with the likelihood of rape, after controlling for endogeneity. Moreover, the deterrent effect of Uber on sexual assault is entirely driven by the taxi-sparse areas, namely outside Manhattan. This study sheds light on the potential of ride-sharing platforms and sharing economy to improve social welfare beyond economic gains.

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

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.

The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector Machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the IEEE 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or non-vulnerable and determines System Vulnerability Status.

This article focuses on the design of safe and attack-resilient Cyber-Physical Systems (CPS) equipped with multiple sensors measuring the same physical variable. A malicious attacker may be able to disrupt system performance through compromising a subset of these sensors. Consequently, we develop a precise and resilient sensor fusion algorithm that combines the data received from all sensors by taking into account their specified precisions. In particular, we note that in the presence of a shared bus, in which messages are broadcast to all nodes in the network, the attacker’s impact depends on what sensors he has seen before sending the corrupted measurements. Therefore, we explore the effects of communication schedules on the performance of sensor fusion and provide theoretical and experimental results advocating for the use of the Ascending schedule, which orders sensor transmissions according to their precision starting from the most precise. In addition, to improve the accuracy of the sensor fusion algorithm, we consider the dynamics of the system in order to incorporate past measurements at the current time. Possible ways of mapping sensor measurement history are investigated in the article and are compared in terms of the confidence in the final output of the sensor fusion. We show that the precision of the algorithm using history is never worse than the no-history one, while the benefits may be significant. Furthermore, we utilize the complementary properties of the two methods and show that their combination results in a more precise and resilient algorithm. Finally, we validate our approach in simulation and experiments on a real unmanned ground robot.

Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. Sharing resources between different tenants and the underlying complex technology increase the necessity of transparency and accountability. In this regard, auditing security compliance of the provider's infrastructure against standards, regulations and customers' policies takes on an increasing importance in the cloud to boost the trust between the stakeholders. However, virtualization and scalability make compliance verification challenging. In this work, we propose an automated framework that allows auditing the cloud infrastructure from the structural point of view while focusing on virtualization-related security properties and consistency between multiple control layers. Furthermore, to show the feasibility of our approach, we integrate our auditing system into OpenStack, one of the most used cloud infrastructure management systems. To show the scalability and validity of our framework, we present our experimental results on assessing several properties related to auditing inter-layer consistency, virtual machines co-residence, and virtual resources isolation.

Cloud service providers typically adopt the multi-tenancy model to optimize resources usage and achieve the promised cost-effectiveness. Sharing resources between different tenants and the underlying complex technology increase the necessity of transparency and accountability. In this regard, auditing security compliance of the provider's infrastructure against standards, regulations and customers' policies takes on an increasing importance in the cloud to boost the trust between the stakeholders. However, virtualization and scalability make compliance verification challenging. In this work, we propose an automated framework that allows auditing the cloud infrastructure from the structural point of view while focusing on virtualization-related security properties and consistency between multiple control layers. Furthermore, to show the feasibility of our approach, we integrate our auditing system into OpenStack, one of the most used cloud infrastructure management systems. To show the scalability and validity of our framework, we present our experimental results on assessing several properties related to auditing inter-layer consistency, virtual machines co-residence, and virtual resources isolation.

Authorship attribution is a classical classification problem. We use it here to illustrate the performance of a compression-based measure that relies on the notion of relative compression. Besides comparing with recent approaches that use multiple discriminant analysis and support vector machines, we compare it with the Normalized Conditional Compression Distance (a direct approximation of the Normalized Information Distance) and the popular Normalized Compression Distance. The Normalized Relative Compression (NRC) attained 100% correct classification in the data set used, showing consistency between the compression ratio and the classification performance, a characteristic not always present in other compression-based measures.
 

Authorship attribution is a classical classification problem. We use it here to illustrate the performance of a compression-based measure that relies on the notion of relative compression. Besides comparing with recent approaches that use multiple discriminant analysis and support vector machines, we compare it with the Normalized Conditional Compression Distance (a direct approximation of the Normalized Information Distance) and the popular Normalized Compression Distance. The Normalized Relative Compression (NRC) attained 100% correct classification in the data set used, showing consistency between the compression ratio and the classification performance, a characteristic not always present in other compression-based measures.

In this talk, we investigate applications of Factor Graphs to automatically generate attack signatures from security logs and domain expert knowledge. We demonstrate advantages of Factor Graphs over traditional probabilistic graphical models such as Bayesian Networks and Markov Random Fields in modeling security attacks. We illustrate Factor Graphs models using case studies of real attacks observed in the wild and at the National Center for Supercomputing Applications. Finally, we investigate how factor functions, a core component of Factor Graphs, can be constructed automatically to potentially improve detection accuracy and allow generalization of trained Factor Graph models in a variety of systems.

Side Channel Attacks (SCA) have proven to be a practical threat to the security of embedded systems, exploiting the information leakage coming from unintended channels concerning an implementation of a cryptographic primitive. Given the large variety of embedded platforms, and the ubiquity of the need for secure cryptographic implementations, a systematic and automated approach to deploy SCA countermeasures at design time is strongly needed. In this paper, we provide an overview of recent compiler-based techniques to protect software implementations against SCA, making them amenable to automated application in the development of secure-by-design systems.

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.

Internet Engineering Task Force (IETF) is working on 6LoW-PAN standard which allows smart devices to be connected to Internet using large address space of IPV6. 6LoWPAN acts as a bridge between resource constrained devices and the Internet. The entire IoT space is vulnerable to local threats as well as the threats from the Internet. Due to the random deployment of the network nodes and the absence of tamper resistant shield, the resource constrained IoT elements face potential insider attacks even in presence of front line defense mechanism that involved cryptographic protocols. To detect such insidious nodes, an Intrusion Detection System (IDS) is required as a second line of defense. In this paper, we attempt to analyze such potential insider attacks, while reviewing the IDS based countermeasures. We attempt to propose a baseline for designing IDS for 6LoWPAN based IoT system.

The Security Behavior Intentions Scale (SeBIS) measures the computer security attitudes of end-users. Because intentions are a prerequisite for planned behavior, the scale could therefore be useful for predicting users' computer security behaviors. We performed three experiments to identify correlations between each of SeBIS's four sub-scales and relevant computer security behaviors. We found that testing high on the awareness sub-scale correlated with correctly identifying a phishing website; testing high on the passwords sub-scale correlated with creating passwords that could not be quickly cracked; testing high on the updating sub-scale correlated with applying software updates; and testing high on the securement sub-scale correlated with smartphone lock screen usage (e.g., PINs). Our results indicate that SeBIS predicts certain computer security behaviors and that it is a reliable and valid tool that should be used in future research.

WSN is a collection of tiny nodes that used to absorb the natural phenomenon from the operational environment and send it to the control station to extract the useful information. In most of the Existing Systems, the assumption is that the operational environment of the sensor nodes deployed is trustworthy and secure by means of some cryptographic operations and existing trust model. But in the reality it is not the case. Most of the existing systems lacks in providing reliable security to the sensor nodes. To overcome the above problem, in this paper, Beta Reputation and Direct Trust Model (BRDT) is the combination of Direct Trust and Beta Reputation Trust for secure communication in Wireless Sensor Networks. This model is used to perform secure routing in WSN. Overall, the method provides an efficient trust in WSN compared to existing methods.

In this study, we report on techniques and analyses that enable us to capture Internet-wide activity at individual IP address-level granularity by relying on server logs of a large commercial content delivery network (CDN) that serves close to 3 trillion HTTP requests on a daily basis. Across the whole of 2015, these logs recorded client activity involving 1.2 billion unique IPv4 addresses, the highest ever measured, in agreement with recent estimates. Monthly client IPv4 address counts showed constant growth for years prior, but since 2014, the IPv4 count has stagnated while IPv6 counts have grown. Thus, it seems we have entered an era marked by increased complexity, one in which the sole enumeration of active IPv4 addresses is of little use to characterize recent growth of the Internet as a whole. With this observation in mind, we consider new points of view in the study of global IPv4 address activity. Our analysis shows significant churn in active IPv4 addresses: the set of active IPv4 addresses varies by as much as 25% over the course of a year. Second, by looking across the active addresses in a prefix, we are able to identify and attribute activity patterns to networkm restructurings, user behaviors, and, in particular, various address assignment practices. Third, by combining spatio-temporal measures of address utilization with measures of traffic volume, and sampling-based estimates of relative host counts, we present novel perspectives on worldwide IPv4 address activity, including empirical observation of under-utilization in some areas, and complete utilization, or exhaustion, in others.

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.

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.

With the use of external cloud services such as Google Docs or Evernote in an enterprise setting, the loss of control over sensitive data becomes a major concern for organisations. It is typical for regular users to violate data disclosure policies accidentally, e.g. when sharing text between documents in browser tabs. Our goal is to help such users comply with data disclosure policies: we want to alert them about potentially unauthorised data disclosure from trusted to untrusted cloud services. This is particularly challenging when users can modify data in arbitrary ways, they employ multiple cloud services, and cloud services cannot be changed. To track the propagation of text data robustly across cloud services, we introduce imprecise data flow tracking, which identifies data flows implicitly by detecting and quantifying the similarity between text fragments. To reason about violations of data disclosure policies, we describe a new text disclosure model that, based on similarity, associates text fragments in web browsers with security tags and identifies unauthorised data flows to untrusted services. We demonstrate the applicability of imprecise data tracking through BrowserFlow, a browser-based middleware that alerts users when they expose potentially sensitive text to an untrusted cloud service. Our experiments show that BrowserFlow can robustly track data flows and manage security tags for documents with no noticeable performance impact.

We develop a novel model-based hardware-in-the-loop (HIL) framework for optimising energy consumption of embedded software controllers. Controller and plant models are specified as networks of parameterised timed input/output automata and translated into executable code. The controller is encoded into the target embedded hardware, which is connected to a power monitor and interacts with the simulation of the plant model. The framework then generates a power consumption model that maps controller transitions to distributions over power measurements, and is used to optimise the timing parameters of the controller, without compromising a given safety requirement. The novelty of our approach is that we measure the real power consumption of the controller and use thus obtained data for energy optimisation. We employ timed Petri nets as an intermediate representation of the executable specification, which facilitates efficient code generation and fast simulations. Our framework uniquely combines the advantages of rigorous specifications with accurate power measurements and methods for online model estimation, thus enabling automated design of correct and energy-efficient controllers.