# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 2 Trends in Malware Detection
### Malware Detection with Deep Learning Based Methods
[PERSON] et al. used Neural nets and extracted Strings and PE file characteristics but did not cope with obfuscation and did not produce good accuracy in such situations. Ref. [59] implemented the idea of a multitasking learning model which was trained for seven classification tasks for malware image classification. The implemented model by [59] consisted of 5 CNN layers with PRelu activation function. Ref. [60] have explored the advantages of using transfer learning in the domain of malware identification. Their research focused on utilizing transfer learning for extracting the features of malware dataset. They made use of an already trained deep learning model (trained over ImageNet) and finally classified the malware into their respective families. Figure 5 summarizes the types of deep learning algorithms used by researchers over the years and Table 3 summarizes the performance metrics used by researchers while using deep learning based methods for malware detection. Critical analysis of all the surveyed papers that implemented deep learning algorithms, emphasizes the grave need of using a large dataset to produce reliable results. Deep learning architectures heavily make use of supervised learning that requires a large no. of labeled examples for training the model as mentioned by [61]. Using the small dataset does not help the model to learn the features properly during the training phase which leads to non-reliable results. Another aspect that got unveiled during this survey referred to the fact that this large dataset is supposed to contain a large no. of examples for each class that must be identified by the trained model. And processing the bulk of data in deep learning needs powerful hardware, high computational processing power, and high training time which diminishes the chance of applying the trained models to real-time data. Because of these unavoidable features of deep learning models, the market could not get successful in  
Figure 5: Deep Learning Techniques Used for Malware Detection. replacing the signature-based anti-malware systems with artificially intelligent systems. Therefore, researchers shifted their direction of research from developing deep models for feature learning to finding out the possibilities of developing models that can work over small datasets. In the quest of achieving the previously mentioned objective, researchers explored the concept of Few Shot Learning (FSL) which is based on meta learning with a focus on learning the strategy of how to learn the meaningful properties of data. Meta learning utilizes the concept of transfer learning (multi-task learning) and semi-supervised or unsupervised learning approaches which need a few examples for the training. And thus, according to [62], the meta learning model can be trained with the help of prior knowledge. Meta learning based algorithms that are being used in malware analysis include Few Shot Learning (FSL), One shot Learning (OSL), and Zero Shot Learning (ZSL). Figure 6 shows the relationship between machine learning and meta learning models. Major advantages of meta learning based algorithms are listed in Figure 7.