# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Conventional Machine Learning Based Methods
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title** & **Author** & \multicolumn{3}{c}{**Data Samples Used**} & \multicolumn{3}{c}{**Performance Metrics Used**} \\ \cline{3-6}  & & **Source** & **Malicious** & **Benign** & \\ \hline Support Vector Machine for malware analysis and classification & [PERSON], [PERSON] & N\& Platform & - & - & Classification Accuracy = 0.9498 & \\  & [PERSON], [PERSON] & N\& Platform & - & - & Sensitivity = 0.9774 & \\  & & N\& Platform & - & - & Specificity = 0.8971 & \\  & & & & & AUC = 0.9901 & \\  & & & & & FI = 0.9623 & \\  & & & & & Precision = 0.9475 & \\ \hline Improving the detection of malware behavior using simplified data dependent API call graph & [PERSON], [PERSON], [PERSON] & V\&Heavens & 75 & 10 & \begin{tabular}{l} Detection Rate = 98.6\% \\ Accuracy = 98.8\% \\ False Alarm = 0\% \\ \end{tabular} \\ \hline Dynamic VSA: a framework for malware detection based on register contents & [PERSON], [PERSON], [PERSON] & Windows XP system, Salehi Program Files Folder, and Private Repository & 850 & 390 & \begin{tabular}{l} TP = 0.988 \\ FP = 0.125 \\ Recall = 0.988 \\ Precision = 0.888 \\ F-Measure = 0.940 \\ Accuracy = 0.930 \\ \end{tabular} \\ \hline Novel feature extraction, selection, and fusion for effective malware family classification & [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] & Microsoft’s Malware classification challenge & 21,741 & 0 & Accuracy, Logloss \\ \hline Probabilistic inference on integrity for access behavior based malware detection & [PERSON], [PERSON], [PERSON], [PERSON] & Windows XP SP3 V\&F257 & 534 & TPR, AUC \\ \hline Robust and effective malware detection through quantitative data flow graph metrics & [PERSON], [PERSON], [PERSON] & Legitimate app downloads Malicia & 6994 & 513 & \
\begin{tabular}{l} Detection Rate, FPR, \\ Precision, F-Measure \\ \end{tabular} \\ \hline An alternative to NCD for large sequences, Lempel Ziv Jaccard distance & [PERSON], [PERSON] & Industry Partner & 237,349 & 240,000 & Balanced Accuracy \\ \hline Proposing a HMM-based approach to detect metamorphic malware & [PERSON], [PERSON], [PERSON], [PERSON] & [PERSON]\&Heavens & - & - &
\begin{tabular}{l} Detection Rate = 0.9803 \\ FPR = 0.0058 \\ Accuracy = 0.9833 \\ \end{tabular} \\ \hline Heuristic metamorphic malware detection based on statistics of assembly instructions using classification algorithms & [PERSON], [PERSON], [PERSON], [PERSON] & Windows XP system and Program Files folder Self-generated metamorphic malware & 280 & 550 & Accuracy \\ \hline A malware similarity testing framework & [PERSON], [PERSON] & Sampled from security incidents & 85 & 0 & PR Curve \\ \hline A behavior based malware variant classification technique & [PERSON], [PERSON], [PERSON] & Anubis Website & 330,248 & 0 & Similarity measure \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets and Performance Metrics Used in Literature Proposing Machine Learning Methods for Malware Detection.