3 research outputs found
MMF: A loss extension for feature learning in open set recognition
Open set recognition (OSR) is the problem of classifying the known classes,
meanwhile identifying the unknown classes when the collected samples cannot
exhaust all the classes. There are many applications for the OSR problem. For
instance, the frequently emerged new malware classes require a system that can
classify the known classes and identify the unknown malware classes. In this
paper, we propose an add-on extension for loss functions in neural networks to
address the OSR problem. Our loss extension leverages the neural network to
find polar representations for the known classes so that the representations of
the known and the unknown classes become more effectively separable. Our
contributions include: First, we introduce an extension that can be
incorporated into different loss functions to find more discriminative
representations. Second, we show that the proposed extension can significantly
improve the performances of two different types of loss functions on datasets
from two different domains. Third, we show that with the proposed extension,
one loss function outperforms the others in terms of training time and model
accuracy
Deep Learning and Open Set Malware Classification: A Survey
As the Internet is growing rapidly these years, the variant of malicious
software, which often referred to as malware, has become one of the major and
serious threats to Internet users. The dramatic increase of malware has led to
a research area of not only using cutting edge machine learning techniques
classify malware into their known families, moreover, recognize the unknown
ones, which can be related to Open Set Recognition (OSR) problem in machine
learning. Recent machine learning works have shed light on Open Set Recognition
(OSR) from different scenarios. Under the situation of missing unknown training
samples, the OSR system should not only correctly classify the known classes,
but also recognize the unknown class. This survey provides an overview of
different deep learning techniques, a discussion of OSR and graph
representation solutions and an introduction of malware classification systems
Open-world Machine Learning: Applications, Challenges, and Opportunities
Traditional machine learning especially supervised learning follows the
assumptions of closed-world learning i.e., for each testing class a training
class is available. However, such machine learning models fail to identify the
classes which were not available during training time. These classes can be
referred to as unseen classes. Whereas, open-world machine learning deals with
arbitrary inputs (data with unseen classes) to machine learning systems.
Moreover, traditional machine learning is static learning which is not
appropriate for an active environment where the perspective and sources, and/or
volume of data are changing rapidly. In this paper, first, we present an
overview of open-world learning with importance to the real-world context.
Next, different dimensions of open-world learning are explored and discussed.
The area of open-world learning gained the attention of the research community
in the last decade only. We have searched through different online digital
libraries and scrutinized the work done in the last decade. This paper presents
a systematic review of various techniques for open-world machine learning. It
also presents the research gaps, challenges, and future directions in
open-world learning. This paper will help researchers to understand the
comprehensive developments of open-world learning and the likelihoods to extend
the research in suitable areas. It will also help to select applicable
methodologies and datasets to explore this further