Multiple instance learning for malware classification
نویسندگان
چکیده
منابع مشابه
Multiple Instance Learning for Malware Classification
This work addresses classification of unknown binaries executed in sandbox by modeling their interaction with system resources (files, mutexes, registry keys and communication with servers over the network) and error messages provided by the operating system, using vocabulary-based method from the multiple instance learning paradigm. It introduces similarities suitable for individual resource t...
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Multiple-Instance learning is a way of mod-eling ambiguity in supervised learning examples. Each example is a bag of instances, but only the bag is labeled-not the individual instances. A bag is labeled negative if all the instances are negative, and positive if at least one of the instances in positive. We apply the Multiple-Instance learning framework to the problem of learning how to classif...
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Analysis of audiovisual human behavior observations is a common practice in behavioral sciences. It is generally carried through by expert annotators who are asked to evaluate several aspects of the observations along various dimensions. This can be a tedious task. We propose that automatic classification of behavioral patterns in this context can be viewed as a multiple instance learning probl...
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Multiple Instance Learning (MIL) is a popular learning technique in various vision tasks including image classification. However, most existing MIL methods do not consider the problem of insufficient examples in the given target category. In this case, it is difficult for traditional MIL methods to build an accurate classifier due to the lack of training examples. Motivated by the empirical suc...
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ژورنال
عنوان ژورنال: Expert Systems with Applications
سال: 2018
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2017.10.036