# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 2 Literature Review  
Deep learning is a subfield of machine learning that has garnered increasing attention from academic and corporate researchers in recent years. Originally developed for use in computer vision, deep learning has now expanded to other fields [14]. Voice recognition and NLP used as extra feature engineering methods are two areas where deep learning has  
Figure 2: Worldwide malware cyber-attacks.  
demonstrated very strong potential [15]. Deep learning is different from more traditional, shallow machine learning methods in that it can benefit from starting with the raw data itself, thereby doing away with the time-consuming task of feature generation by hand [16]. Deep neural network CNN-LSTMs also collect representations from different levels, which are abstractions of different layers, by stacking several layers in a hierarchical way. Tuning parameters across several layers allows for training a model with more granularity [17].  
Several types of deep learning networks have been used in a wide range of settings, employing a broad variety of datasets [18]. Creating networks and unit operations in a number of different methods is required for capturing and learning features from a wide range of sources [19]. For example, CNNs (convolutional neural networks) excel in processing visual and aural data because of their capacity to operate in a two-dimensional plane. RNNs have excelled in the field of natural language processing. Even with a small amount of training data, RBM (restricted Boltzmann machines)-built generic DBN (deep belief networks) are good at modeling and fine-tuning convergence speed [20]. Malware is a major threat to cyber security on all scales, if not identified quickly after it has been introduced. Malware is proliferating at an alarming rate, making it difficult for even highly experienced network administrators to recognize it, much less typical internet users. Traditional detection approaches focused on feature extraction and comparison are becoming more useless as a result [21].  
[PERSON]'s [22] research results depict that the accuracy of the [PERSON] [22] proposed CNN method for malware detection is 91.01%. Malicious software comes in various forms, including source code, binary files, Perl scripts, shell scripts, instructions, and others, and its complexity has only grown over the years, making detection even more of a Herculean task. Because of the added intricacy, mistakes in judgement are punished more heavily.  
The [PERSON] [22] CNN method testing accuracy, which is 91.01% even when the FPR is 21%, remained same as in training of the CNN-LSTM method for malware detection. In this study, convolutional neural networks (CNNs), one of the most successful deep learning methodologies, are used. The experimental findings show a success rate of over 90% in differentiating malicious from safe programs. In addition to recognizing binary and source code, CNN was able to identify harmful code that had been introduced into otherwise safe code, as shown by the experiment. In today's tough network environment, IT workers need a way to take preventative steps and plan for future cyberattacks, so this study suggests a practical way to find malware at its source.  
[PERSON] proposed a CNN-LSTM method tested on the small dataset. It is clear from the results that by decreasing FPR to 0.2% the accuracy of the [PERSON] [22] proposed LSTM-CNN method increased to 99.6%, but this accuracy was recorded when the dataset is completely normalized and the dataset is small.