Biblio

In this paper, we present Deola, an Online system for Author Entity Linking with DBLP. Unlike most existing entity linking systems which focus on linking entities with Wikipedia and depend largely on the special features associated with Wikipedia (e.g., Wikipedia articles), Deola links author names appearing in the web document which belongs to the domain of computer science with their corresponding entities existing in the DBLP network. This task is helpful for the enrichment of the DBLP network and the understanding of the domain-specific document. This task is challenging due to name ambiguity and limited knowledge existing in DBLP. Given a fragment of domain-specific web document belonging to the domain of computer science, Deola can return the mapping entity in DBLP for each author name appearing in the input document.

Moving target defense (MTD) is an emerging paradigm in which system defenses dynamically mutate in order to decrease the overall system attack surface. Though the concept is promising, implementations have not been widely adopted. The field has been actively researched for over ten years, and has only produced a small amount of extensively adopted defenses, most notably, address space layout randomization (ASLR). This is despite the fact that there currently exist a variety of moving target implementations and proofs-of-concept. We suspect that this results from the moving target controls breaking critical system dependencies from the perspectives of users and administrators, as well as making things more difficult for attackers. As a result, the impact of the controls on overall system security is not sufficient to overcome the inconvenience imposed on legitimate system users. In this paper, we analyze a successful MTD approach. We study the control's dependency graphs, showing how we use graph theoretic and network properties to predict the effectiveness of the selected control.

Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.

In settings where data instances are generated sequentially or in streaming fashion, online learning algorithms can learn predictors using incremental training algorithms such as stochastic gradient descent. In some security applications such as training anomaly detectors, the data streams may consist of private information or transactions and the output of the learning algorithms may reveal information about the training data. Differential privacy is a framework for quantifying the privacy risk in such settings. This paper proposes two differentially private strategies to mitigate privacy risk when training a classifier for anomaly detection in an online setting. The first is to use a randomized active learning heuristic to screen out uninformative data points in the stream. The second is to use mini-batching to improve classifier performance. Experimental results show how these two strategies can trade off privacy, label complexity, and generalization performance.

Browser fingerprinting is a widely used technique to uniquely identify web users and to track their online behavior. Until now, different tools have been proposed to protect the user against browser fingerprinting. However, these tools have usability restrictions as they deactivate browser features and plug-ins (like Flash) or the HTML5 canvas element. In addition, all of them only provide limited protection, as they randomize browser settings with unrealistic parameters or have methodical flaws, making them detectable for trackers. In this work we demonstrate the first anti-fingerprinting strategy, which protects against Flash fingerprinting without deactivating it, provides robust and undetectable anti-canvas fingerprinting, and uses a large set of real word data to hide the actual system and browser properties without losing usability. We discuss the methods and weaknesses of existing anti-fingerprinting tools in detail and compare them to our enhanced strategies. Our evaluation against real world fingerprinting tools shows a successful fingerprinting protection in over 99% of 70.000 browser sessions.

The demand for trained cybersecurity operators is growing more quickly than traditional programs in higher education can fill. At the same time, unemployment for returning military veterans has become a nationally discussed problem. We describe the design and launch of New Skills for a New Fight (NSNF), an intensive, one-year program to train military veterans for the cybersecurity field. This non-traditional program, which leverages experience that veterans gained in military service, includes recruitment and selection, a base of knowledge in the form of four university courses in a simultaneous cohort mode, a period of hands-on cybersecurity training, industry certifications and a practical internship in a Security Operations Center (SOC). Twenty veterans entered this pilot program in January of 2016, and will complete in less than a year's time. Initially funded by a global financial services company, the program provides veterans with an expense-free preparation for an entry-level cybersecurity job.

Instead of developing single-server software system for the powerful computers, the software is turning from large single-server to multi-server system such as distributed system. This change introduces a new challenge for the software quality measurement, since the current software analysis methods for single-server software could not observe and assess the correlation among the components on different nodes. In this paper, a new dynamic cohesion approach is proposed for distributed system. We extend Calling Network model for distributed system by differentiating methods of components deployed on different nodes. Two new cohesion metrics are proposed to describe the correlation at component level, by extending the cohesion metric of single-server software system. The experiments, conducted on a distributed systems-Netflix RSS Reader, present how to trace the various system functions accomplished on three nodes, how to abstract dynamic behaviors using our model among different nodes and how to evaluate the software cohesion on distributed system.

We focus on the question of distributed control of electricity distribution networks in the face of security attacks to Distributed Energy Resources (DERs). Our attack model includes strategic manipulation of DER set-points by an external hacker to induce a sudden compromise of a subset of DERs connected to the network. We approach the distributed control design problem in two stages. In the first stage, we model the attacker-defender interaction as a Stackelberg game. The attacker (leader) disconnects a subset of DERs by sending them wrong set-point signals. The distribution utility (follower) response includes Volt-VAR control of non-compromised DERs and load control. The objective of the attacker (resp. defender) is to maximize (resp. minimize) the weighted sum of the total cost due to loss of frequency regulation and the cost due to loss of voltage regulation. In the second stage, we propose a distributed control (defender response) strategy for each local controller such that, if sudden supply-demand mismatch is detected (for example, due to DER compromises), the local controllers automatically respond based on their respective observations of local fluctuations in voltage and frequency. This strategy aims to achieve diversification of DER functions in the sense that each uncompromised DER node either contributes to voltage regulation (by contributing reactive power) or to frequency regulation (by contributing active power). We illustrate the effectiveness of this control strategy on a benchmark network.

We analyze a dynamic oligopoly model with strategic sellers and buyers/consumers over a finite horizon. Each seller has private information described by a finite-state Markov process; the Markov processes describing the sellers' information are mutually independent. At the beginning of each time/stage t the sellers simultaneously post the prices for their good; subsequently, consumers make their buying decisions; finally, after the buyers' decisions are made, a public signal, indicating the buyers' consumption experience from each seller's good becomes available and the whole process moves to stage t + 1. The sellers' prices, the buyers' decisions and the signal indicating the buyers' consumption experience are common knowledge among buyers and sellers. This dynamic oligopoly model arises in online shopping and dynamic spectrum sharing markets. The model gives rise to a stochastic dynamic game with asymmetric information. Using ideas from the common information approach, we prove the existence of common information based equilibria. We obtain a sequential decomposition of the game and we provide a backward induction algorithm to determine common information-based equilibria that are perfect Bayesian equilibria. We illustrate our results with an example.

The Industrial Internet promises to radically change and improve many industry's daily business activities, from simple data collection and processing to context-driven, intelligent and pro-active support of workers' everyday tasks and life. The present paper first provides insight into a typical industrial internet application architecture, then it highlights one fundamental arising contradiction: “Who owns the data is often not capable of analyzing it”. This statement is explained by imaging a visionary data supply chain that would realize some of the Industrial Internet promises. To concretely implement such a system, recent standards published by The Open Group are presented, where we highlight the characteristics that make them suitable for Industrial Internet applications. Finally, we discuss comparable solutions and concludes with new business use cases.

In cyberspace, availability of the resources is the key component of cyber security along with confidentiality and integrity. Distributed Denial of Service (DDoS) attack has become one of the major threats to the availability of resources in computer networks. It is a challenging problem in the Internet. In this paper, we present a detailed study of DDoS attacks on the Internet specifically the attacks due to protocols vulnerabilities in the TCP/IP model, their countermeasures and various DDoS attack mechanisms. We thoroughly review DDoS attacks defense and analyze the strengths and weaknesses of different proposed mechanisms.

This paper considers a decentralized switched control problem where exact conditions for controller synthesis are obtained in the form of semidefinite programming (SDP). The formulation involves a discrete-time switched linear plant that has a nested structure, and whose system matrices switch between a finite number of values according to finite-state automation. The goal of this paper is to synthesize a commensurately nested switched controller to achieve a desired level of 2-induced norm performance. The nested structures of both plant and controller are characterized by block lower-triangular system matrices. For this setup, exact conditions are provided for the existence of a finite path-dependent synthesis. These include conditions for the completion of scaling matrices obtained through an extended matrix completion lemma.When individual controller dimensions are chosen at least as large as the plant, these conditions reduce to a set of linear matrix inequalities. The completion lemma also provides an algorithm to complete closed-loop scaling matrices, leading to inequalities for  ontroller synthesis that are solvable either algebraically or numerically through SDP.

The power system forms the backbone of a modern society, and its security is of paramount importance to nation's economy. However, the power system is vulnerable to intelligent attacks by attackers who have enough knowledge of how the power system is operated, monitored and controlled. This paper proposes a game theoretic approach to explore and evaluate strategies for the defender to protect the power systems against such intelligent attacks. First, a risk assessment is presented to quantify the physical impacts inflicted by attacks. Based upon the results of the risk assessment, this paper represents the interactions between the attacker and the defender by extending the current zero-sum game model to more generalized game models for diverse assumptions concerning the attacker's motivation. The attacker and defender's equilibrium strategies are attained by solving these game models. In addition, a numerical illustration is demonstrated to warrant the theoretical outcomes.

The digital authentication security technology is widely used in the current brand cosmetics as key anti-counterfeiting technology, yet this technology is prone to "false security", "hard security" and "non-security" phenomena. This paper researches the current cosmetics brand distribution channels and sales methods also analyses the cosmetics brands' demand for RFID technology anti-counterfeiting security system, then proposes a security system based on RFID technology for brand cosmetics. The system is based on a typical distributed RFID tracking and tracing system which is the most widely used system-EPC system. This security system based on RFID technology for brand cosmetics in the paper is a visual information management system for luxury cosmetics brand. It can determine the source of the product timely and effectively, track and trace products' logistics information and prevent fake goods and gray goods getting into the normal supply chain channels.

The optimal design of a fault-tolerant quantum computer involves finding an appropriate balance between the burden of large-scale integration of noisy components and the load of improving the reliability of hardware technology. This balance can be evaluated by quantitatively modeling the execution of quantum logic operations on a realistic quantum hardware containing limited computational resources. In this work, we report a complete performance simulation software tool capable of (1) searching the hardware design space by varying resource architecture and technology parameters, (2) synthesizing and scheduling a fault-tolerant quantum algorithm within the hardware constraints, (3) quantifying the performance metrics such as the execution time and the failure probability of the algorithm, and (4) analyzing the breakdown of these metrics to highlight the performance bottlenecks and visualizing resource utilization to evaluate the adequacy of the chosen design. Using this tool, we investigate a vast design space for implementing key building blocks of Shor’s algorithm to factor a 1,024-bit number with a baseline budget of 1.5 million qubits. We show that a trapped-ion quantum computer designed with twice as many qubits and one-tenth of the baseline infidelity of the communication channel can factor a 2,048-bit integer in less than 5 months.

Forming, in a decentralized fashion, an optimal network topology while balancing multiple, possibly conflicting objectives like cost, high performance, security and resiliency to viruses is a challenging endeavor. In this paper, we take a game-formation approach to network design where each player, for instance an autonomous system in the Internet, aims to collectively minimize the cost of installing links, of protecting against viruses, and of assuring connectivity. In the game, minimizing virus risk as well as connectivity costs results in sparse graphs. We show that the Nash Equilibria are trees that, according to the Price of Anarchy (PoA), are close to the global optimum, while the worst-case Nash Equilibrium and the global optimum may significantly differ for small infection rate and link installation cost. Moreover, the types of trees, in both the Nash Equilibria and the optimal solution, depend on the virus infection rate, which provides new insights into how viruses spread: for high infection rate τ, the path graph is the worst- and the star graph is the best-case Nash Equilibrium. However, for small and intermediate values of τ, trees different from the path and star graphs may be optimal.