Titlebook: Machine Learning under Malware Attack; Raphael Labaca-Castro Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive li

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Introductionomous vehicles [Jan+20]. Although ML models have been ubiquitously deployed to make life easier, not all of the algorithms have been vetted enough to ensure their safety, which is an often neglected aspect when designing solutions.

障碍

Background by defining the origin of malicious applications to understand their impact. Next, we present the PE format, which is the type of binary that we use as an input object during our experimental evaluation. We then explore AML and review the literature, starting with early implementations in the secur

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FAMEaluation (FAME) [LR22], which can be observed in Fig. 1.1 under a homonymous name. We define the notation and threat model for our research by describing the adversary’s knowledge, objectives, and capabilities. Since the requirements vary depending on the attack settings, they will be presented indi

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Generative Adversarial Netsn order to optimize opposite goals in a zero-sum game framework. Although in this scenario one network profits from the other’s loss, GANs become better at their predictions by means of cooperation rather than competitiveness. The generator learns the statistics from the training set to produce new

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Comparison of Strategiesunderstand the advantages and disadvantages of each strategy. As depicted in Fig. 1.1, our goal is to create an initial benchmark to evaluate adversarial examples in the context of malware using PE files. Notably, since gradient-based approaches (i.e., GRIPE in Chapter 8) and GAN attacks (i.e., GAIN

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nd the interaction of polarized radiation with natural scenes and to search for useful discriminants to classify targets at a distance. In order to study the polarization response of various targets, the matrix models (i.e., 2 × 2 coherent Jones and Sinclair and 4 × 4 average power density Mueller (

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