Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies write by National Academies of Sciences, Engineering, and Medicine. This book was released on 2019-08-22. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies available in PDF, EPUB and Kindle. The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies write by National Academies of Sciences, Engineering, and Medicine. This book was released on 2019-08-22. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies available in PDF, EPUB and Kindle. The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.
Adversarial Machine Learning
Adversarial Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Adversarial Machine Learning write by Anthony D. Joseph. This book was released on 2019-02-21. Adversarial Machine Learning available in PDF, EPUB and Kindle. 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.
Machine Learning Algorithms
Machine Learning Algorithms - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Algorithms write by Fuwei Li. This book was released on 2022-11-14. Machine Learning Algorithms available in PDF, EPUB and Kindle. This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.
Studying the Robustness of Machine Learning-based Malware Detection Models
Studying the Robustness of Machine Learning-based Malware Detection Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Studying the Robustness of Machine Learning-based Malware Detection Models write by Ahmed Abusnaina. This book was released on 2022. Studying the Robustness of Machine Learning-based Malware Detection Models available in PDF, EPUB and Kindle. With the rise of the popularity of machine learning (ML), it has been shown that ML-based classifiers are susceptible to adversarial examples and concept drifting, where a small modification in the input space may result in misclassification. The ever-evolving nature of the data, the behavioral and pattern shifting over time not only lessened the trust in the machine learning output but also created a barrier for its usage in critical applications. This dissertation builds toward analyzing machine learning-based malware detection systems, including the detection and mitigation of adversarial malware examples. In particular, we first introduce two black-box adversarial attacks on control flow-based malware detectors, exposing the vulnerability of graph-based malware detection systems. Further, we propose DL-FHMC, fine-grained hierarchical learning technique for robust malware detection, leveraging graph mining techniques alongside pattern recognition for adversarial malware detection. Enabling machine learning in critical domains is not limited to the detection of adversarial examples in laboratory settings, but also extends to exploring the existence of adversarial behavior in the wild. Toward this, we investigate the attack surface of malware detection systems, shedding light on the vulnerability of the underlying learning algorithms and industry-standard machine learning malware detection systems against adversaries in both IoT and Windows environments. Toward robust malware detection, we investigate software pre-processing and monotonic machine learning. In addition, we explore potential exploitation caused by actively retraining malware detection models. We uncover a previously unreported malicious to benign detection performance trade-off, causing the malware to revive and be classified as a benign or different malicious family. This behavior leads to family labeling inconsistencies, hindering the efforts toward malicious families’ understanding. Overall, this dissertation builds toward robust malware detection, by analyzing and detecting adversarial examples. We highlight the vulnerability of industry-standard applications to black-box adversarial settings, including the continuous evolution of malware over time.
