August 2019
Intermediate to advanced
342 pages
9h 35m
English
Content preview from Hands-On Artificial Intelligence for Cybersecurity
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Random Forest Malware Classifier
The following is an example of the Random Forest Malware Classifier implemented with the scikit-learn library:
import pandas as pdimport numpy as npfrom sklearn import *malware_dataset = pd.read_csv('../datasets/MalwareArtifacts.csv', delimiter=',')# Extracting artifacts samples fields "AddressOfEntryPoint" and# "DllCharacteristics"samples = malware_dataset.iloc[:, [0,4]].valuestargets = malware_dataset.iloc[:, 8].valuesfrom sklearn.model_selection import train_test_splittraining_samples, testing_samples, training_targets, testing_targets = train_test_split(samples, targets,test_size=0.2)rfc = ensemble.RandomForestClassifier(n_estimators=50)rfc.fit(training_samples, training_targets)accuracy = rfc.score(testing_samples, ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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