# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 1 Introduction  
According to the Panda Security report [1], hackers are involved in creating around 230,000 malware samples daily, a number expected to grow in the coming years. According to an FBI report [2], ransomware is considered to be one of the fastest-growing threats, with over 4000 ransomware attacks occurring every day since 2016. Ransomware is capable of targeting home users, small and large businesses, and has the potential to cause the loss ofsensitive information temporarily or permanently according to [3]. Critical infrastructure is the most luring target for the ones who are well versed with the damages that can be caused by ransomware. Ransomware is the type of malware that uses the encryption module to encrypt the data and makes it unusable for the user [4]. Over the past few decades ransomware has affected not only small businesses but has victimized big companies like FedEx, Nissan, Russian and German railways, and NHS organizations in the UK according to Ref. [5]. According to a report [6] produced by Kaspersky, spam emails are the constant features of phishing, and this trend is unlikely to change soon. Symantec's Internet Security Threat report of 2019 [7] stated that supply chains remained a soft target, with attacks increasing by 78% in 2019 compared to the previous year. The same report mentions blocking 69 million cryptojacking events in 2018, four times increase compared to 2017. Small businesses are severely affected by cyber-attacks and according to statistics in 2019, 40% of small companies were attacked, out of which only 13% could detect and mitigate the attacks [8]. Due to economic losses caused by cyber-attacks, 60% of small companies collapsed. Accenture reports that the US $2.4 M is spent by companies to support malware detection and defense from web-based attacks. Cyber-attacks have heavily created chaos in critical infrastructure as well. State-sponsored attackers had been found involved in launching attacks over industrial control systems lately. One of the biggest examples of such malware is Stuxnet which was designed to choke the working of the Iranian Nuclear Power Plant's centrifuges [9,10]. Cyber physical systems are almost applied in all critically important areas such as traffic lights, health care, power generation, water industry, transportation system, etc. [11]. Communication of these cyber physical systems with network make them vulnerable and many stealthy attacks launching different malicious payloads can be expected easily by looking at the statistics [12]. Malfunctioning of such significantly important systems can cause severe accidents and damages. To protect the cyber physical systems working in all crucial areas, researchers have been trying their level best to device an anti-malware system that can protect them. There are many tools and anti-virus products available in the market for the detection of malware and cyberattacks, however, they have their inherent shortcomings. Anti-virus products work over the signatures of malware, and the signature database needs to be constantly updated. This technique also does not work for zero-day attacks and for the new variants of existing malware (which can have a different signature). Various strategies have been implemented to speed up the real time detection of different types of malware as explained in Appendix A.1 so that the effect of the malware can be mitigated. A taxonomy of malware analysis is explained in Appendix A.2 and is illustrated in Figure 1: static analysis focuses on detecting a malicious file without executing it, whereas dynamic analysis works by first executing the file. A hybrid strategy involves a combination of both static and dynamic analysis  
Various approaches have been reported in the literature to detect malicious behavior and files, involving: (i) statistical data analysis-based research for malware classification; (ii) machine learning methods (including Deep Learning) for malware detection and identification.