# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Deep Learning Based Methods  
Deep Learning is a specialized form of machine learning in the domain of Artificial Intelligence (AI) that applies deep artificial neural networks also famous as deep neural networks. They are the techniques of machine learning that simulate the process of learning by a human brain. The human brain consists of cells which are referred to as neurons in neural networks. Similarly, in a human brain, all the cells are connected through axons and dendrites with the connection region known as synapses. These connections when found in ANN (Artificial Neural Networks), contain weights to behave as the connections between nerve cells in the human brain. Figure A2 (Appendix B) shows the human brain and simulated version of the human brain through the artificial neural network. The major difference between conventional neural networks and deep neural networks is the number of layers. Deep neural networks make use of many hidden layers for the high-level abstraction of data. They can learn the features of data. This process of feature engineering is carried out with the help of a big number of examples input to the deep learning-based algorithm which leads to the production of results in the form of classification, identification, or generation of data after learning the most suitable features during feature engineering. The major motivation for using deep learning in various fields was to organize and analyze a large amount of data. Different areas where deep networks are preferred to be used include image processing, speech processing, healthcare, and with the increase in cyber space, now even cybersecurity. Depending upon its features, this domain can be further categorized into different sub-domains as shown in Figure 4. All features of PE files hold some significance in defining degree of maliciousness in a particular file. Features from the header and Imports, all play \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Title**} & \multirow{2}{*}{**Author**} & \multicolumn{3}{c}{**Data Samples Used**} & \multicolumn{2}{c}{**Performance Metrics Used**} \\ \cline{3-6}  & & **Source** & **Malicious** & **Benign** & \\ \hline Scaling Malware Execution & & & Security Company & & \\ with Sequential Multi & & Perdisci & and Large Research & 1,651,906 & 0 & Jaccard Index \\ Hypothesis Testing & & & Institute & & & \\ \hline Fast malware classification by & & [PERSON]. [PERSON], [PERSON], V. & Legitimate apps & & & \\ automated behavioral graph & & [PERSON], [PERSON] & Anubis Sandbox & 300 & 80 & Similarity measurement \\ matching & & [PERSON] & & & & \\ \hline Automated malware & & & & & & \\ classification based on & & & & & \\ network behavior & & & & & \\ \hline Malware function & & & & & \\ classification using APIs in & & & & & \\ initial behavior & & & & & \\ \hline Feature selection and & & & & & Micro Precision, Micro Recall, \\ extraction for malware & & [PERSON], [PERSON], & & & Micro Specificity, Macro \\ classification & & [PERSON]. [PERSON], [PERSON] & & & & \\ \hline High fidelity, behavior based & & & & & \\ automated malware analysis & & [PERSON], [PERSON] & & & \\ and classification & & [PERSON], [PERSON] & & & \\ \hline Clustering for malware & & & & & \\ classification & & & & & \\ \hline Towards Automatic & & & & & \\ Reverse Engineering of Large & & & & & \\ Datasets of Binaries & & Jackdaw & & & \\ \hline Subroutine based detection of & & & & & \\ APT malware & & & & & \\ \hline A static signal processing & & & & & \\ based malware triage & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)a significant role in defining the nature of PE file as malicious or benign. Ref. [41] made use of LSTM for the selection of optimal features of PEs. These optimal features were selected to train a deep learning based model for detecting malicious PE file. Refs.