# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Deep Learning Based Methods

Table 5: Limitations of Surveyed Papers Proposing Machine Learning Based Solutions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title** & **Author** & **Year** & **Weakness/Limitation** \\ \hline Early Stage Malware Prediction & [PERSON], et al. & 2018 & The system should be tested for large data. This approach can be a failure if the attacker comes to know that file is being monitored in the first 5 s so this can be evaded. \\ \hline DLAMD: A Deep Learning Framework for Intelligent Malware Detection & [PERSON], [PERSON], et al. & 2016 & Sparsity constraints are not imposed on SAE which can improve malware detection. \\ \hline eXpose: A Character-Level Convolutional with Embeddings for Detecting Malicious URLs, File Paths and Registry Key & [PERSON], [PERSON], and [PERSON]. & 2017 & The computational cost of training on long strings is very high. This approach labeled any sample that had 0 occurrences in malware data as benign, and the rest was labeled malware. So, strings, file paths, and registry keys due to less training data can decrease this model’s generalizability. \\ \hline Robust Malware Detection for the Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning & [PERSON], [PERSON], [PERSON], and [PERSON]. & 2018 & Dataset was small for training the neural network which implies that the network could not learn the features at its best. \\ \hline Detection of Malicious Code Variants Based on Deep Learning & [PERSON], [PERSON], et al. & 2018 & The model required all the input images to be of fixed size due to which images could have lost meaningful information while image processing. \\ \hline Malware Identification Using visualization images and deep learning & [PERSON], [PERSON], [PERSON], and [PERSON] & 2018 & Detection of packed, encrypted malware or malware using anti-debugging and anti-disembling approaches is not performed. Real time data consist of all these kind of malware, therefore network might not work well with real time data. \\ \hline End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation State Malware & [PERSON], [PERSON], [PERSON], and [PERSON]. & 2018 & Due to the unavailability of nation-state APT, the proposed classifier is not evaluated for it. Moreover, static features provided by Cuckoo sandbox are not verified \\ \hline Empowering Convolutional Networks for Malware Classification and Analysis & [PERSON], [PERSON], et al. & 2017 & Results of only static malware analysis are used. Code obfuscation can affect the proposed approach. A system unknown family of malware can affect the system’s performance. \\ \hline Malware Detection Based on Deep Learning of Behavior Graphs & [PERSON] et al. & 2019 & The time of execution for extracting API calls is not mentioned. If the time of execution would have been small, then the results would not be reliable \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Limitations of Surveyed Papers Proposing Deep Learning Based solutions for Malware Detection.