# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 3 Malware Detection and Classification with Machine Learning  
Machine learning is a methodology in which we make the computer learn about the problem statement. We make it learn by giving it the data and the information in observation and real-world interactions. In the architecture of malware detection proposed by [PERSON] et al. [30], they used data from the VXheaven, Nothing, and VirusShare datasets on models and applied 10-fold cross-validation to it. Multiple classification algorithms were used, including support vector machine, naive Bayes and random forest. The performance was evaluated using the FPR, FNR, TPR, accuracy and precision. The naive Bayes, SVM, and random forest gave the accuracy of 96.8%, 97.68%, 99.11%, respectively [31]. Among the three, random forest showed the highest accuracy, with the lowest FPR & FNP.  
Figure 1: Different types of malware detection techniques.  
[PERSON] et al., 2018 [32] presented a novel approach where the self-organizing feature maps are used to classify and reduce the overfitting between the malware and benign files occurring during training of the dataset. This dataset is gathered with the help of the VirusTotal API. Random forest, BayesNet, MLP, and SVM are among the classifiers used in this paper. The random forest classifier has the highest accuracy, with a score of 98 percent. However, due to overfitting, it was reduced by 12% when applied with various datasets. The problem of overfitting was addressed with the program Self-Organizing Feature Map (SOFM), which is a classifier based on the ANN technique, and the accuracy increased by 7%.  
[PERSON] et al., 2018 [33] suggested a novel mal classifier technique. CTU13 and the Stratosphere IPS project were the datasets used. With 95.5 percent accuracy, KNN and Random Forest were employed for malware classification and training.  
[PERSON] et al., 2017 [34] proposed a novel malware classification method based on clustering techniques such as k-means, expectation-maximization, and hidden Markov models. The information was gathered from the Malicia website. Among all of the methods, expectation-maximization had the best accuracy.  
[PERSON] et al. [35] provided an approach in which the unknown malware instances are classified into a cluster according to their families. The shared nearest neighbor clustering algorithm was used, giving an accuracy of 98.9% for available malware and 86.7% for unavailable malware.  
Neural networks were employed by [PERSON] et al., 2017 [36] to classify unknown malware. The methods used for malware classification were perceptron, decision tree, closest centroid, stochastic gradient, multilayer perceptron, and random forest, with the random forest approach providing the best average of training and testing accuracy.  
[PERSON] et al., 2014 [37] presented a framework to classify unknown malware extracted from static and dynamic features. Multilayer perceptron, IB1, decision tree, and random forest were the four classifiers used to classify the data. With a 99.58 percent accuracy rate, random forest was the most accurate.  
[PERSON] et al., 2009 [38] proposed a method for classifying malware using printable strings. Naive Bayes, support vector machine, IB1, random forest, and decision trees were used on the retrieved features. The AdaBoostM1 meta-classifier was employed to increase efficiency. With a 97 percent accuracy, random forest and IB1 produced superior outcomes.  
[PERSON] et al., 2010 [39], To improve the performance, Naive Bayes, SMO, Random forest and J48, were used to the retrieved features in this work. Random forest classifiers were the most accurate, with a 96.2 percent accuracy rate.