Detecting Malware Without Feature Engineering Using Deep Learning
Detecting Malware Without Feature Engineering Using Deep Learning
Nowadays, machine learning is routinely used in the detection of network attacks and the identification of malicious programs. In most ML-based approaches, each analysis sample (such as an executable program, an office document, or a network request) is analyzed and a number of features are extracted. For example, in the case of a binary program, one might extract the names of the library functions being invoked, the length of the sections of the executable, and so forth.
Then, a machine learning algorithm is given as input a set of known benign and known malicious samples (called the ground truth). The algorithm creates a model that, based on the values of the features contained in the samples, is the ground truth dataset, and the model is then able to classify known samples correctly. If the dataset from which the algorithm has learned is representative of the real-world domain, and if the features are relevant for discriminating between benign and malicious programs, chances are that the learned model will generalize and allow for the detection of previously unseen malicious samples.
The Role of Feature Engineering
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