Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses.

https://dl.acm.org/doi/pdf/10.1145/3128572.3140444

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

Deep learning is at the heart of the current rise of artificial intelligence. In the field of computer vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas, deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently led to a large influx of contributions in this direction. This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Traditionally, interference is considered harmful. Wireless networks strive to avoid scheduling multiple transmissions at the same time in order to prevent interference. This paper adopts the opposite approach; it encourages strategically picked senders to interfere. Instead of forwarding packets, routers forward the interfering signals. The destination leverages network-level information to cancel the interference and recover the signal destined to it. The result is analog network coding because it mixes signals not bits.So, what if wireless routers forward signals instead of packets? Theoretically, such an approach doubles the capacity of the canonical 2-way relay network. Surprisingly, it is also practical. We implement our design using software radios and show that it achieves significantly higher throughput than both traditional wireless routing and prior work on wireless network coding.

/pdf/embracing-wireless-interference-analog-network-coding-1vuvzsncu8.pdf

Embracing wireless interference: analog network coding

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples . Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.

Adversarial Examples: Attacks and Defenses for Deep Learning

Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations--small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial samples from their normal and noisy counterparts. We investigate model confidence on adversarial samples by looking at Bayesian uncertainty estimates, available in dropout neural networks, and by performing density estimation in the subspace of deep features learned by the model. The result is a method for implicit adversarial detection that is oblivious to the attack algorithm. We evaluate this method on a variety of standard datasets including MNIST and CIFAR-10 and show that it generalizes well across different architectures and attacks. Our findings report that 85-93% ROC-AUC can be achieved on a number of standard classification tasks with a negative class that consists of both normal and noisy samples.

Detecting Adversarial Samples from Artifacts.

We propose a probabilistic key distribution protocol for vehicular network that alleviates the burden of traditional public-key infrastructures. Roadside units act as trusted nodes and are used for secret-sharing among vehicles in their vicinity. Secure communication is immediately possible between these vehicles with high probability. Our performance evaluation, which uses both analysis and simulation, shows that high reliability and short dissemination time can be achieved with low complexity.

https://mateboban.net/almeida_globecom_key_distr.pdf

Probabilistic key distribution in vehicular networks with infrastructure support

As an alternative to the algebraic network codes prevalent in the literature, we consider Arithmetic Network Codes (henceforth abbreviated as ANCs), i.e., codes in which interior nodes perform finite precision arithmetic over the real or complex fields. We suggest two applications where using such codes can be advantageous. First, we demonstrate that the multi-resolution behaviour of ANCs potentially outperforms that of algebraic network codes. Second, the interfering and fading nature of wireless channels naturally results in complex linear combinations of transmissions, analogous to ANCs. We then characterize the multicast rates achievable by ANCs, and demonstrate that for high precision arithmetic these are equivalent to those obtained by algebraic network codes. We show the connection between the performance of ANCs and the numerical conditioning of network transform matrices. Using this, we obtain upper and lower bounds on the number of significant bits required to perform the finite precision arithmetic in terms of the network parameters. We compare this with simulation results for randomized and deterministic design of ANCs.

"Real" and "Complex" Network Codes: Promises and Challenges

Building expressive encrypted databases that can scale to large volumes of data while enjoying formal security guarantees has been one of the holy grails of security and cryptography research. Searchable Encryption (SE) is considered to be an attractive implementation choice for this goal: It naturally supports basic database queries such as point, join, group-by and range, and is very practical at the expense of well-defined leakage such as search and access pattern. Nevertheless, recent attacks have exploited these leakages to recover the plaintext database or the posed queries, casting doubt to the usefulness of SE in encrypted systems. Defenses against such leakage-abuse attacks typically require the use of Oblivious RAM or worst-case padding—such countermeasures are however quite impractical. In order to efficiently defend against leakage-abuse attacks on SE-based systems, we propose SEAL, a family of new SE schemes with adjustable leakage. In SEAL, the amount of privacy loss is expressed in leaked bits of search or access pattern and can be defined at setup. As our experiments show, when protecting only a few bits of leakage (e.g., three to four bits of access pattern), enough for existing and even new more aggressive attacks to fail, SEAL query execution time is within the realm of practical for real-world applications (a little over one order of magnitude slowdown compared to traditional SE-based encrypted databases). Thus, SEAL could comprise a promising approach to build efficient and robust encrypted databases.

/pdf/seal-attack-mitigation-for-encrypted-databases-via-daqyugohom.pdf

{SEAL}: Attack Mitigation for Encrypted Databases via Adjustable Leakage

The benefits of network coding in terms of throughput, security and robustness are well understood for a large class of networks, such as wireless mesh networks and peer-to-peer systems We ask if similar performance enhancements can be attained in satellite based communications Our feasibility study focuses on GEO satellite communication systems including regenerative payloads and multi spot-beam satellites In addition, we consider LEO satellite constellations with multi-path scenarios Our analysis reveals that several network coding techniques are worthy of consideration for new generation satellite systems with highly dynamic and unpredictable behavior

Saurabh Shintre

Papers

Detecting Adversarial Samples from Artifacts.

Probabilistic key distribution in vehicular networks with infrastructure support

"Real" and "Complex" Network Codes: Promises and Challenges

{SEAL}: Attack Mitigation for Encrypted Databases via Adjustable Leakage

How feasible is network coding in current satellite systems