###### Abstract  
Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MalwD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization). The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MalwD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy. malware detection and categorization; pattern matching; binary and multi-class classification  
202313202331320  
Windows, Linux, macOS, etc., are vulnerable to such attacks and could be exploited using various file formats, e.g., Portable Executables (PE), Executable and Linkable Format (ELF), Portable Document Format (PDF), etc., we focus on malware with PE files in Windows OS domain because it holds 76.33% of the global OS market, followed by macOS 14.64% and Linux 2.42% [1] and has shown to be extremely popular among the end users. Consequently, the malware in the PE file format to attack Windows-based computer systems is the most studied threat in the wild [2]. To this end, Kaspersky lab reported the detection of 360,000 malware files in 2020 (including 90% Windows PE malwares) [3]. The same trend was shown to be continued in the year 2021 as well [4]. To combat this threat, researchers and security experts have proposed various approaches, such as signature-based [5; 6; 7], behaviour-based [8], and machine learning-based [9; 10] detection, to identify and prevent the installation of malware in PE files on Windows-based computer systems. Signature-based detection [5] relies on known malware's unique patterns or characteristics to identify and block them. In contrast, behaviour-based detection monitors the behavior of software programs to detect suspicious activities that may indicate the presence of malware. These schemes depend on comparing the extracted signature from the current file to all the signatures maintained in the database of earlier collected and confirmed malware. Whether the current file is malware or benign depends on the similarity of the signatures. The main flaw of this approach is its dependence on the previously collected and confirmed malware signatures; hence, it can only detect the known malware. However, with the continuous and ever-increasing malware types, these schemes are not considered to be effective any more [11]. Consequently, the focus of the present research has been diverted to machine learning-based malware detection schemes [12; 13]. Machine learning, including deep learning, has shown to be extremely successful in various domains such as computer vision, biometrics, speech recognition, etc. To this end, these schemes are also evaluated in the PE malware detection domain. Due to their generalizing capability, these models have been shown to generalize well on unseen testing malware samples. The schemes have shown to be accurate; however, their performance measurement (in terms of training and decision time) and their vulnerability to adversarial attacks are often neglected. In this paper, we propose MalwD&C--a lightweight, accurate, and quick machine-learning-based approach for PE malware detection and prevention.