# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 1 Introduction
### Contribution  
* The main focus of this research study is to use the CNN-LSTM method and detect the malware of IoT devices in the emerging technology [4]. * The proposed CNN-LSTM method aims to detect malware in real-time. * The author divided the dataset in to two sets, one set is employed for the training the dataset which is composed of 70% of the data, the second set represents 30% of the data and is used to test the trained CNN-LSTM model. * Our deep learning model achieves an accuracy rate of 99% without the use of any complicated feature engineering. Worldwide, cybercriminals pose a threat to businesses, governments, and individuals by spreading malicious software and stealing private information [5]. Every day, hundreds of hackers employ malicious software to breach networks, steal data, and make unlawful financial transactions. Because of this, the security of personal information is becoming a pressing concern in the scientific world. This study uses data mining and machine learning classification methods to identify malicious software and prevent its access to sensitive information [6]. To accomplish this, we perform an analysis of signature-based and anomaly-based features in order to provide a trustworthy and effective way for malware classification and detection. The experimental results have proven that the proposed strategy is superior to the alternatives. Multiple cyberattacks, such as those depicted in Figure 1, can be launched in the context of cyberwarfare. Today's malware is prevalent and complex; as a result, it presents a significant threat to the security of online platforms [7]. Malicious software, or malware, is software with the intent of harming a computer or network in some way, most commonly for financial gain. Malware attacks increasingly target IoT devices, medical gear, and infrastructure control systems in both natural and constructed settings [8]. Modern spyware is notoriously hard to detect since it constantly updates its code and behavior. The proliferation of malware has reached a stage where traditional signature-based defenses are ineffective. There needs to be a broader set of safeguards in place instead of just ignoring these cyber threats [9]. The identification and classification of malware have become more difficult tasks in recent years [9]. Polymorphism and metamorphism are only two of the many strategies used by malware writers to confuse detectors and avoid \"pattern matching\" detection. Malware analysis might benefit from monitoring actual infection rates and pattern mining based on the virus changing behaviors in the field [10]. Figure 1: Different types of malware attacks. Malware attackers have developed a number of automated malware-generating toolkits, allowing for the quick manufacture of malicious programs with an infinite number of variations that may easily circumvent standard pattern matching detections. Since this is the case, we need cutting-edge methods to automatically analyze malware, find trends, and stop common ways of avoiding detection. In recent years, deep learning's popularity has surged as a tool for tackling difficult pattern recognition and machine learning problems [11]. With the help of these local representations of features, it is possible to learn and remember higher-level abstractions. In this paper, we compare the order of API calls to the syntax of a sentence. We proposed a new model for malware classification that makes use of deep neural networks and a model of natural language processing to improve classification accuracy. The number of organizations (including corporations, banks, universities, social media accounts, and government departments) that rely on internet connectivity is increasing at an exponential rate. This [12] growth might be jeopardized by cybercriminals who use malicious software and network threats in their attacks. When triggered, malware instructs a computer to perform abnormal operations, which may compromise the victim's data or software.