## 1 Introduction  
Malware typically refers to malicious code that intends to deface confidential data, financial information, or any digital resources of a computer system. The evaluation of malicious software is a never-ending process [1; 2]. In particular, the ease of communication, like the availability of the internet, digital assets, and online transactions, enables the evolution of malware faster than ever [3; 4]. The casualties inflicted by malware have also increased in recent years. According to a statistic [5], the global inflicted damage yielded by malware is calculated at SAR 6 trillion in the year 2021. Also, this inflicted cost has been predicted to rise to SAR 10 trillion in 2025. The increasing threat of malware highlights the need for innovative and effective malware detection measures that effectively work with low-power edge computing infrastructures. In the past, malware was identified using signature-based techniques. These methods compare suspicious files to pre-established malware signatures through static detection [6; 7; 8; 9]. It is important to note that this type of malware defense requires a significant amount of handcrafted malware feature samples, such as text signs, regular expressions, filenames, and byte codes [2]. Despite the use of these features, these methods can only detect a few malware variants that match the predetermined features. Additionally, anti-analysis techniques like obfuscation, packing, and polymorphism can easily bypass these defenses with minor modifications. Furthermore, traditional approaches are computationally expensive, requiring a secure environment to analyze each suspicious file. These limitations highlight the need for more dynamic and practical methods of malware classification. Recent research on detecting malware places significant emphasis on developing AI-powered solutions that leverage deep learning techniques to overcome the limitations of traditional approaches [1; 2; 10]. These approaches treat malware analysis as a classical image classification task, representing the malware binaries as images. Over the past decade, several innovative works with complex network architectures have been introduced [3; 11; 12; 13; 14], which have shown considerable improvements over traditional methods. To evaluate the practicality of these existing methods in developing robust malware detection, especially for edge platforms, we assessed the performance of various classification methods, including specialized malware classification methods, using a standard benchmark malware dataset as illustrated in Figure 1.  
As Figure 1 illustrates, the existing deep networks, explicitly, the malware classification methods, failed to achieve a high fidelity rate in the benchmark dataset. Our initial evaluation found that the existing malware classification methods fail to extract and utilize salient features from the malware image. In addition to that, the state-of-the-art (SOTA) methods are developed primarily for desktop or server-class hardware [23; 24]. Due to computational and optimization limitations, the existing malware detection works assume that malware detection should be performed on high-performance computing devices like servers via API [25; 26]. However, detecting malware on local low-power devices in real-world scenarios is more practical, as it can address the threat without any external dependency [27; 28]. Deploying efficient and independent detection mechanisms can revolutionize future solutions, such as integrating them into the secure computing frameworks of 5G/6G infrastructures [29; 30]. The current limitation of existing methods and countless applications of efficient malware detection motivated us to develop a robust classifier with an optimization strategy for real-world usage. This study proposes a novel deep network to classify the malware classes. Our proposed architecture incorporates a two-branch deep network to leverage local-global attention on different image scales.