activity
20192021
most citedExpansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques

11 citations · 20 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2021

Privacy Protection of Grid Users Data with Blockchain and Adversarial Machine Learning

Ibrahim Yilmaz, Kavish Kapoor, Ambareen Siraj +1

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…

cs.CR20211 cited

Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning

Ibrahim Yilmaz, Ambareen Siraj, Denis Ulybyshev

Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C\&C) server communications during cyber attacks. Blacklists of known/identified C\&C…

cs.LG2020

Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning

ibrahim Yilmaz, Ambareen Siraj

More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication…

cs.CV20208 cited

Practical Fast Gradient Sign Attack against Mammographic Image Classifier

Ibrahim Yilmaz

Artificial intelligence (AI) has been a topic of major research for many years. Especially, with the emergence of deep neural network (DNN), these studies have been tremendously su…

cs.LG201911 cited

Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques

Ibrahim Yilmaz, Rahat Masum

Machine learning techniques help to understand patterns of a dataset to create a defense mechanism against cyber attacks. However, it is difficult to construct a theoretical model…