Robust Filtering Schemes for Machine Learning Systems to Defend Adversarial Attack PDF Download
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Author: Kishor Datta Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through a diverse set of filters before making the final decision. My experimental results illustrated that the proposed active learning-based defense strategy could mitigate adaptive or advanced adversarial manipulations both in input and after with the model decision for a wide range of ML attacks by higher accuracy. Moreover, the output decision boundary inspection using a classification technique automatically reaffirms the reliability and increases the trustworthiness of any ML-Based decision support system. Unlike other defense strategies, my defense technique does not require adversarial sample generation, and updating the decision boundary for detection makes the defense systems robust to traditional adaptive attacks..
Author: Kishor Datta Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through a diverse set of filters before making the final decision. My experimental results illustrated that the proposed active learning-based defense strategy could mitigate adaptive or advanced adversarial manipulations both in input and after with the model decision for a wide range of ML attacks by higher accuracy. Moreover, the output decision boundary inspection using a classification technique automatically reaffirms the reliability and increases the trustworthiness of any ML-Based decision support system. Unlike other defense strategies, my defense technique does not require adversarial sample generation, and updating the decision boundary for detection makes the defense systems robust to traditional adaptive attacks..
Author: Kishor Datta Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Defenses against adversarial attacks are essential to ensure the reliability of machine learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. I proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through a diverse set of filters before making the final decision. My experimental results illustrated that the proposed active learning-based defense strategy could mitigate adaptive or advanced adversarial manipulations both in input and after with the model decision for a wide range of ML attacks by higher accuracy. Moreover, the output decision boundary inspection using a classification technique automatically reaffirms the reliability and increases the trustworthiness of any ML-Based decision support system. Unlike other defense strategies, my defense technique does not require adversarial sample generation, and updating the decision boundary for detection makes the defense systems robust to traditional adaptive attacks.
Author: Aneesh Sreevallabh Chivukula
Publisher: Springer Nature
ISBN: 3030997723
Category : Computers
Languages : en
Pages : 316
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Book Description
A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Author: Rachid Guerraoui
Publisher: Springer Nature
ISBN: 9819706882
Category :
Languages : en
Pages : 180
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Book Description
Author: Yongxuan Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Thanks to the increasing availability of high-quality data and the success of deep learning algorithms, machine learning (ML)-based classifiers have become increasingly appealing and investigated against sophisticated attacks in complex cyber-physical systems like the smart grid. However, many of these techniques rely on the assumption that the training and testing datasets share the same distribution and class labels in a stationary environment. As such assumption may fail to hold when the system dynamics shift and new threat variants emerge in a non-stationary environment, the capability of trained ML models to adapt in complex operating scenarios will be critical to their deployment in real-world applications. Using cyber-physical attack detection in the smart grid as the targeted application, this research aims to leverage transfer learning-based strategies to improve the robustness of ML classifiers against variations in threat types, locations, and timing in a complex dynamic CPS. To this end, this research investigates and develops domain-adversarial transfer learning schemes for robust intrusion detection against smart grid attacks. The main contributions include: (i) A domain-adversarial transfer learning scheme with customized classifiers for attack detection based on realistic smart grid data collected from a hardware-in-the-loop testbed; (ii) A semi-supervised transfer learning to transfer the knowledge of limited known attack incidences to detect returning threats at a later time with different system dynamics; (iii) A divergence-based transferability analysis and a spatiotemporal domain-adversarial transfer learning scheme for robust detection against spatial and temporal variants. Experiments were conducted on standardized IEEE benchmarks, and the results have demonstrated the promising capability of domain adversarial transfer learning to improve ML robustness against system and attack variations.
Author: Jonas Rauber
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Machine learning has made enormous progress in the last five to ten years. We can now make a computer, a machine, learn complex perceptual tasks from data rather than explicitly programming it. When we compare modern speech or image recognition systems to those from a decade ago, the advances are awe-inspiring. The susceptibility of machine learning systems to small, maliciously crafted adversarial perturbations is less impressive. Almost imperceptible pixel shifts or background noises can completely derail their performance. While humans are often amused by the stupidity of artificial intelligence, engineers worry about the security and safety of their machine learning applications, and scientists wonder how to make machine learning models more robust and more human-like. This dissertation summarizes and discusses advances in three areas of adversarial robustness. First, we introduce a new type of adversarial attack against machine learning models in real-world black-box scenarios. Unlike previous attacks, it does not require any insider knowledge or special access. Our results demonstrate the concrete threat caused by the current lack of robustness in machine learning applications. Second, we present several contributions to deal with the diverse challenges around evaluating adversarial robustness. The most fundamental challenge is that common attacks cannot distinguish robust models from models with misleading gradients. We help uncover and solve this problem through two new types of attacks immune to gradient masking. Misaligned incentives are another reason for insufficient evaluations. We published joint guidelines and organized an interactive competition to mitigate this problem. Finally, our open-source adversarial attacks library Foolbox empowers countless researchers to overcome common technical obstacles. Since robustness evaluations are inherently unstandardized, straightforward access to various attacks is more than a technical convenience; it promotes thorough evaluations. Third, we showcase a fundamentally new neural network architecture for robust classification. It uses a generative analysis-by-synthesis approach. We demonstrate its robustness using a digit recognition task and simultaneously reveal the limitations of prior work that uses adversarial training. Moreover, further studies have shown that our model best predicts human judgments on so-called controversial stimuli and that our approach scales to more complex datasets.
Author: Anthony D. Joseph
Publisher: Cambridge University Press
ISBN: 1108325874
Category : Computers
Languages : en
Pages : 341
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Book Description
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309494508
Category : Computers
Languages : en
Pages : 99
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Book Description
In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.
Author: Samarth Gupta (S.M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Book Description
Efficient operation and control of modern day urban systems such as transportation networks is now more important than ever due to huge societal benefits. Low cost network-wide sensors generate large amounts of data which needs to processed to extract useful information necessary for operational maintenance and to perform real-time control. Modern Machine Learning (ML) systems, particularly Deep Neural Networks (DNNs), provide a scalable solution to the problem of information retrieval from sensor data. Therefore, Deep Learning systems are increasingly playing an important role in day-to-day operations of our urban systems and hence cannot not be treated as standalone systems anymore. This naturally raises questions from a security viewpoint. Are modern ML systems robust to adversarial attacks for deployment in critical real-world applications? If not, then how can we make progress in securing these systems against such attacks? In this thesis we first demonstrate the vulnerability of modern ML systems on a real world scenario relevant to transportation networks by successfully attacking a commercial ML platform using a traffic-camera image. We review different methods of defense and various challenges associated in training an adversarially robust classifier. In terms of contributions, we propose and investigate a new method of defense to build adversarially robust classifiers using Error-Correcting Codes (ECCs). The idea of using Error-Correcting Codes for multi-class classification has been investigated in the past but only under nominal settings. We build upon this idea in the context of adversarial robustness of Deep Neural Networks. Following the guidelines of code-book design from literature, we formulate a discrete optimization problem to generate codebooks in a systematic manner. This optimization problem maximizes minimum hamming distance between codewords of the codebook while maintaining high column separation. Using the optimal solution of the discrete optimization problem as our codebook, we then build a (robust) multi-class classifier from that codebook. To estimate the adversarial accuracy of ECC based classifiers resulting from different codebooks, we provide methods to generate gradient based white-box attacks. We discuss estimation of class probability estimates (or scores) which are in itself useful for real-world applications along with their use in generating black-box and white-box attacks. We also discuss differentiable decoding methods, which can also be used to generate white-box attacks. We are able to outperform standard all-pairs codebook, providing evidence to the fact that compact codebooks generated using our discrete optimization approach can indeed provide high performance. Most importantly, we show that ECC based classifiers can be partially robust even without any adversarial training. We also show that this robustness is simply not a manifestation of the large network capacity of the overall classifier. Our approach can be seen as the first step towards designing classifiers which are robust by design. These contributions suggest that ECCs based approach can be useful to improve the robustness of modern ML systems and thus making urban systems more resilient to adversarial attacks.
Author: Hossein Hosseini
Publisher:
ISBN:
Category :
Languages : en
Pages : 111
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Book Description
Deep neural networks have achieved remarkable success over the last decade in a variety of tasks. Such models are, however, typically designed and developed with the implicit assumption that they will be deployed in benign settings. With the increasing use of learning systems in security-sensitive and safety-critical application, such as banking, medical diagnosis, and autonomous cars, it is important to study and evaluate their performance in adversarial settings. The security of machine learning systems has been studied from different perspectives. Learning models are subject to attacks at both training and test phases. The main threat at test time is evasion attack, in which the attacker subtly modifies input data such that a human observer would perceive the original content, but the model generates different outputs. Such inputs, known as adversarial examples, has been used to attack voice interfaces, face-recognition systems and text classifiers. The goal of this dissertation is to investigate the test-time vulnerabilities of machine learning systems in adversarial settings and develop robust defensive mechanisms. The dissertation covers two classes of models, 1) commercial ML products developed by Google, namely Perspective, Cloud Vision, and Cloud Video Intelligence APIs, and 2) state-of-the-art image classification algorithms. In both cases, we propose novel test-time attack algorithms and also present defense methods against such attacks.
