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FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques
Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process.
Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning
Utilities around the world are reported to invest a total of around 30 billion over the next few years for installation of more than 300 million smart meters, replacing traditional analog meters [1].
Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning
Additionally, we propose a new method using adversarial machine learning to generate never-before-seen malware-related domain families that can be used to illustrate the shortcomings of machine learning algorithms in this regard.
Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning
Detecting the occupancy of a home is straightforward with time of use information as there is a strong correlation between occupancy and electricity usage.
Practical Fast Gradient Sign Attack against Mammographic Image Classifier
For instance; a benign sample can be modified as a malicious sample or a malicious one can be altered as benign while this modification can not be recognized by human observer.
Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques
Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks.
