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Academic literature on the topic 'Adversarial Channels'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Adversarial Channels"

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Suresh, Vinayak, Eric Ruzomberka, Chih-Chun Wang, and David J. Love. "Causal Adversarial Channels With Feedback Snooping." IEEE Journal on Selected Areas in Information Theory 3, no. 1 (March 2022): 69–84. http://dx.doi.org/10.1109/jsait.2022.3158230.

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Wang, Pengwei, and Reihaneh Safavi-Naini. "A Model for Adversarial Wiretap Channels." IEEE Transactions on Information Theory 62, no. 2 (February 2016): 970–83. http://dx.doi.org/10.1109/tit.2015.2503766.

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Simon, Paul, Scott Graham, Christopher Talbot, and Micah Hayden. "Model for Quantifying the Quality of Secure Service." Journal of Cybersecurity and Privacy 1, no. 2 (May 7, 2021): 289–301. http://dx.doi.org/10.3390/jcp1020016.

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Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based upon the probabilities that adversarial listeners and disruptors may gain access to or manipulate transmitted data. The initial model development, albeit a snap-shot of the network security, provides insights into what may affect end-to-end security and to what degree. The model was compared against the performance and expected security of several point-to-point networks, and three simplified architectures are presented as examples. Message fragmentation and duplication across the available channels provides a security performance trade-space, with an accompanying comprehensive measurement of the QoSS. The results indicate that security may be improved with message fragmentation across multiple channels when compared to the number of adversarial listeners or disruptors. This, in turn, points to the need, in future work, to build a full simulation environment with specific protocols and networks to validate the initial modeled results.
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Anantharamu, Lakshmi, Bogdan S. Chlebus, and Mariusz A. Rokicki. "Adversarial Multiple Access Channels with Individual Injection Rates." Theory of Computing Systems 61, no. 3 (November 28, 2016): 820–50. http://dx.doi.org/10.1007/s00224-016-9725-x.

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Pan, Xuran, Fan Yang, Lianru Gao, Zhengchao Chen, Bing Zhang, Hairui Fan, and Jinchang Ren. "Building Extraction from High-Resolution Aerial Imagery Using a Generative Adversarial Network with Spatial and Channel Attention Mechanisms." Remote Sensing 11, no. 8 (April 15, 2019): 917. http://dx.doi.org/10.3390/rs11080917.

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Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F1-measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
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Safavi-Naini, Reihaneh, and Pengwei Wang. "A Model for Adversarial Wiretap Channels and its Applications." Journal of Information Processing 23, no. 5 (2015): 554–61. http://dx.doi.org/10.2197/ipsjjip.23.554.

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Leung, Debbie, and Graeme Smith. "Communicating Over Adversarial Quantum Channels Using Quantum List Codes." IEEE Transactions on Information Theory 54, no. 2 (February 2008): 883–87. http://dx.doi.org/10.1109/tit.2007.913433.

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Chlebus, Bogdan S., Dariusz R. Kowalski, and Mariusz A. Rokicki. "Maximum throughput of multiple access channels in adversarial environments." Distributed Computing 22, no. 2 (August 27, 2009): 93–116. http://dx.doi.org/10.1007/s00446-009-0086-4.

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Sutanto, Richard Evan, and Sukho Lee. "Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network." Electronics 10, no. 1 (December 30, 2020): 52. http://dx.doi.org/10.3390/electronics10010052.

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Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.
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Ahlswede, Rudolf, Igor Bjelaković, Holger Boche, and Janis Nötzel. "Quantum Capacity under Adversarial Quantum Noise: Arbitrarily Varying Quantum Channels." Communications in Mathematical Physics 317, no. 1 (November 20, 2012): 103–56. http://dx.doi.org/10.1007/s00220-012-1613-x.

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Dissertations / Theses on the topic "Adversarial Channels"

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Akdemir, Kahraman D. "Error Detection Techniques Against Strong Adversaries." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-dissertations/406.

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"Side channel attacks (SCA) pose a serious threat on many cryptographic devices and are shown to be effective on many existing security algorithms which are in the black box model considered to be secure. These attacks are based on the key idea of recovering secret information using implementation specific side-channels. Especially active fault injection attacks are very effective in terms of breaking otherwise impervious cryptographic schemes. Various countermeasures have been proposed to provide security against these attacks. Double-Data-Rate (DDR) computation, dual-rail encoding, and simple concurrent error detection (CED) are the most popular of these solutions. Even though these security schemes provide sufficient security against weak adversaries, they can be broken relatively easily by a more advanced attacker. In this dissertation, we propose various error detection techniques that target strong adversaries with advanced fault injection capabilities. We first describe the advanced attacker in detail and provide its characteristics. As part of this definition, we provide a generic metric to measure the strength of an adversary. Next, we discuss various techniques for protecting finite state machines (FSMs) of cryptographic devices against active fault attacks. These techniques mainly depend on nonlinear robust codes and physically unclonable functions (PUFs). We show that due to the nonuniform behavior of FSM variables, securing FSMs using nonlinear codes is an important and difficult problem. As a solution to this problem, we propose error detection techniques based on nonlinear codes with different randomization methods. We also show how PUFs can be utilized to protect a class of FSMs. This solution provides security on the physical level as well as the logical level. In addition, for each technique, we provide possible hardware realizations and discuss area/security performance. Furthermore, we provide an error detection technique for protecting elliptic curve point addition and doubling operations against active fault attacks. This technique is based on nonlinear robust codes and provides nearly perfect error detection capability (except with exponentially small probability). We also conduct a comprehensive analysis in which we apply our technique to different elliptic curves (i.e. Weierstrass and Edwards) over different coordinate systems (i.e. affine and projective). "
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Ghoreishi, Madiseh Masoud. "Wireless secret key generation versus capable adversaries." Thesis, 2011. http://hdl.handle.net/1828/3766.

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This dissertation applies theories and concepts of wireless communications and signal processing to the security domain to assess the security of a Wireless secret Key Generation (WKG) system against capable eavesdroppers, who employ all the feasible tools to compromise the system’s security. The security of WKG is evaluated via real wireless measurements, where adversary knows and applies appropriate signal processing tools in ordere to predict the generated key with the communicating pair. It is shown that in a broadband stationary wireless communication channel, (e.g. commercial off-the-shelf 802.11 WLAN devices), a capable eavesdropper can recover a large portion of the secret key bits. However, in an Ultra-wideband (UWB) communication, at the same stationary environment, secret key rates of 128 bits per channel probe are achievable.
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(11184744), Vinayak Suresh. "BINARY FEEDBACK IN COMMUNICATION SYSTEMS: BEAM ALIGNMENT, ADVERSARIES AND ENCODING." Thesis, 2021.

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The availability of feedback from the receiver to the transmitter in a communication system can play a significant role. In this dissertation, our focus is specifically on binary or one-bit feedback. First, we study the problem of successive beam alignment for millimeter-wave channels where the receiver sends back only one-bit of information per beam sounding. The sparse nature of the channel allows us to interpret channel sounding as a form of questioning. By posing the alignment problem as a questioning strategy, we describe adaptive (closed-loop) and non-adaptive (open-loop) channel sounding techniques which are robust to erroneous feedback signals caused by noisy quantization. In the second part, we tightly characterize the capacity for two binary stochastic-adversarial mixed noise channels. Specifically, the transmitter (Alice) intends to convey a message to the receiver (Bob) over a binary symmetric channel (BSC) or a binary erasure channel (BEC) in the presence of an adversary (Calvin) who injects additional noise at the channel's input subject to a budget constraint. Calvin is online or causal in that at any point during the transmission, he can infer the bits being sent by Alice and those being received by Bob via a feedback link. Finally in the third part, we study the applicability of binary feedback for encoding and propose the framework of linearly adapting block feedback codes. We also prove a new result for Reed-Muller (RM) codes to demonstrate how an uncoded system can mimic a RM code under this framework, against remarkably large feedback delays.
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"Fundamental Limits of Gaussian Communication Networks in the Presence of Intelligent Jammers." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.55678.

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abstract: The open nature of the wireless communication medium makes it inherently vulnerable to an active attack, wherein a malicious adversary (or jammer) transmits into the medium to disrupt the operation of the legitimate users. Therefore, developing techniques to manage the presence of a jammer and to characterize the effect of an attacker on the fundamental limits of wireless communication networks is important. This dissertation studies various Gaussian communication networks in the presence of such an adversarial jammer. First of all, a standard Gaussian channel is considered in the presence of a jammer, known as a Gaussian arbitrarily-varying channel, but with list-decoding at the receiver. The receiver decodes a list of messages, instead of only one message, with the goal of the correct message being an element of the list. The capacity is characterized, and it is shown that under some transmitter's power constraints the adversary is able to suspend the communication between the legitimate users and make the capacity zero. Next, generalized packing lemmas are introduced for Gaussian adversarial channels to achieve the capacity bounds for three Gaussian multi-user channels in the presence of adversarial jammers. Inner and outer bounds on the capacity regions of Gaussian multiple-access channels, Gaussian broadcast channels, and Gaussian interference channels are derived in the presence of malicious jammers. For the Gaussian multiple-access channels with jammer, the capacity bounds coincide. In this dissertation, the adversaries can send any arbitrary signals to the channel while none of the transmitter and the receiver knows the adversarial signals' distribution. Finally, the capacity of the standard point-to-point Gaussian fading channel in the presence of one jammer is investigated under multiple scenarios of channel state information availability, which is the knowledge of exact fading coefficients. The channel state information is always partially or fully known at the receiver to decode the message while the transmitter or the adversary may or may not have access to this information. Here, the adversary model is the same as the previous cases with no knowledge about the user's transmitted signal except possibly the knowledge of the fading path.
Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2019
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Book chapters on the topic "Adversarial Channels"

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Anantharamu, Lakshmi, Bogdan S. Chlebus, Dariusz R. Kowalski, and Mariusz A. Rokicki. "Medium Access Control for Adversarial Channels with Jamming." In Structural Information and Communication Complexity, 89–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22212-2_9.

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Li, Ling-Long, Guang-Zhong Cao, Hong-Jie Liang, Jiang-Cheng Chen, and Yue-Peng Zhang. "EEG Generation of Virtual Channels Using an Improved Wasserstein Generative Adversarial Networks." In Intelligent Robotics and Applications, 386–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13841-6_36.

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Abate, Carmine, Roberto Blanco, Ștefan Ciobâcă, Adrien Durier, Deepak Garg, Cătălin Hrițcu, Marco Patrignani, Éric Tanter, and Jérémy Thibault. "Trace-Relating Compiler Correctness and Secure Compilation." In Programming Languages and Systems, 1–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_1.

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AbstractCompiler correctness is, in its simplest form, defined as the inclusion of the set of traces of the compiled program into the set of traces of the original program, which is equivalent to the preservation of all trace properties. Here traces collect, for instance, the externally observable events of each execution. This definition requires, however, the set of traces of the source and target languages to be exactly the same, which is not the case when the languages are far apart or when observations are fine-grained. To overcome this issue, we study a generalized compiler correctness definition, which uses source and target traces drawn from potentially different sets and connected by an arbitrary relation. We set out to understand what guarantees this generalized compiler correctness definition gives us when instantiated with a non-trivial relation on traces. When this trace relation is not equality, it is no longer possible to preserve the trace properties of the source program unchanged. Instead, we provide a generic characterization of the target trace property ensured by correctly compiling a program that satisfies a given source property, and dually, of the source trace property one is required to show in order to obtain a certain target property for the compiled code. We show that this view on compiler correctness can naturally account for undefined behavior, resource exhaustion, different source and target values, side-channels, and various abstraction mismatches. Finally, we show that the same generalization also applies to many secure compilation definitions, which characterize the protection of a compiled program against linked adversarial code.
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Anantharamu, Lakshmi, Bogdan S. Chlebus, and Mariusz A. Rokicki. "Adversarial Multiple Access Channel with Individual Injection Rates." In Lecture Notes in Computer Science, 174–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10877-8_15.

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Wang, Wei, Haifeng Hu, and Dihu Chen. "Channel and Constraint Compensation for Generative Adversarial Networks." In Pattern Recognition and Computer Vision, 386–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9_33.

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Rokka Chhetri, Sujit, and Mohammad Abdullah Al Faruque. "Data-Driven Security Analysis Using Generative Adversarial Networks." In Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis, 111–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37962-9_6.

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Du, Zhenjiang, Ning Xie, Zhitao Liu, Xiaohua Zhang, and Yang Yang. "Twin-Channel Gan: Repair Shape with Twin-Channel Generative Adversarial Network and Structural Constraints." In Advances in Computer Graphics, 217–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89029-2_17.

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Xie, Yingpeng, Qiwei Wan, Guozhen Chen, Yanwu Xu, and Baiying Lei. "Retinopathy Diagnosis Using Semi-supervised Multi-channel Generative Adversarial Network." In Ophthalmic Medical Image Analysis, 182–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32956-3_22.

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Mani, Pranav, E. S. Gopi, Hrishikesh Shekhar, and Sharan Chandra. "Generative Adversarial Network and Reinforcement Learning to Estimate Channel Coefficients." In Lecture Notes in Electrical Engineering, 49–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0289-4_4.

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Groza, Bogdan, and Marius Minea. "Bridging Dolev-Yao Adversaries and Control Systems with Time-Sensitive Channels." In Critical Information Infrastructures Security, 167–78. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03964-0_15.

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Conference papers on the topic "Adversarial Channels"

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Suresh, Vinayak, Eric Ruzomberka, and David J. Love. "Stochastic-Adversarial Channels: Online Adversaries With Feedback Snooping." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517968.

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Wang, Pengwei, Reihaneh Safavi-Naini, and Fuchun Lin. "Erasure adversarial wiretap channels." In 2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton). IEEE, 2015. http://dx.doi.org/10.1109/allerton.2015.7447126.

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Kosut, Oliver, and Jorg Kliewer. "Authentication Capacity of Adversarial Channels." In 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613353.

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Zhang, Yihan, Shashank Vatedka, Sidharth Jaggi, and Anand D. Sarwate. "Quadratically Constrained Myopic Adversarial Channels." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437457.

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Bender, Michael A., Martin Farach-Colton, Simai He, Bradley C. Kuszmaul, and Charles E. Leiserson. "Adversarial contention resolution for simple channels." In the 17th annual ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1073970.1074023.

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Aldawsari, Bader A., Bogdan S. Chlebus, and Dariusz R. Kowalski. "Broadcasting on Adversarial Multiple Access Channels." In 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). IEEE, 2019. http://dx.doi.org/10.1109/nca.2019.8935052.

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Safavi-Naini, Reihaneh, and Pengwei Wang. "Codes for limited view adversarial channels." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620229.

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Sangwan, Neha, Mayank Bakshi, Bikash Kumar Dey, and Vinod M. Prabhakaran. "Multiple Access Channels with Adversarial Users." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849729.

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Zhang, Yihan, Shashank Vatedka, and Sidharth Jaggi. "Quadratically Constrained Two-way Adversarial Channels." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174421.

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Zhang, Yihan, Sidharth Jaggi, Michael Langberg, and Anand D. Sarwate. "The Capacity of Causal Adversarial Channels." In 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022. http://dx.doi.org/10.1109/isit50566.2022.9834709.

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