Marco Barreno. Evaluating the Security of Machine Learning Algorithms. PhD thesis, University of California at Berkeley, May 2008.Evaluating the Security of Machine Learning Algorithms - Barreno - 2008 () Citat
8.4 Machine Learning for Cloud Computing 156 8.4.1 Types of Learning Algorithms 156 8.4.1.1 Supervised Learning 156 8.4.1.2 Supervised Learning Approach 156 8.4.1.3 Unsupervised Learning 157 8.4.2 Application on Machine Learning for Cloud Computing 157 8.4.2.1 Image Recognition 157 8.4.2.2 Speech ...
Supervised learning calls on sets of training data, called "ground truth," which are correct question-and-answer pairs. This training helps classifiers, the workhorses of machine learning analysis, to accurately categorize observations. It also helps algorithms, used to organize and orient classifiers...
Machine learning is considerably used in automating digital systems [23,24], which makes it a tempting target for adversaries to attack and potentially harm the interconnected systems. These security violations originated in a distinctive domain associated with the security of machine learning known as ...
as well as deep learning approaches, all of which fall within the broad category of machine learning and are capable of building cybersecurity models for different purposes. In addition, we also exploreadversarialmachine learning, which is the study of how machine learning algorithms are attacked an...
The conference features a diverse set of tracks covering a wide range of topics, including machine learning algorithms and models, security in machine learning, cloud computing and machine learning integration, cybersecurity and threat intelligence, privacy and ethical considerations, secure cloud architect...
AI has great potential to build a better, smarter world, but at the same time faces severe security risks. Due to the lack of security consideration at the early development of AI algorithms, attackers are able to manipulate the inference results in ways that lead to misjudgment. In critical...
Statistical analysis is a core part of machine learning: outputs of machine learning algorithms are often presented in terms of probabilities and confidence intervals. We will touch on some statistical techniques in our discussion of anomaly detection, but we will leave aside questions regarding experim...
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples. A large body of academic research has thoroughly explored the causes of these blind spots, developed sophisticated algorithms for finding them, and proposed a...
Kozlowski, M., Ksiezopolski, B.: A new method of testing machine learning models of detection for targeted DDoS attacks. In: SECRYPT (2021), pp. 728–733 Ughi, G., Abrol, V., Tanner, J.: An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in ...