In the current digital age, security breaches stemming from malicious software (malware) have reached alarming levels, posing substantial threats to individuals, corporations, and governments worldwide. The exponential rise in malware attacks underscores the urgent need for effective detection methods. Traditional approaches to malware detection, which primarily rely on static and dynamic analyses of malware signatures and behavioral patterns, have proven time-consuming and increasingly ineffective in identifying unknown or evolving malware in real-time. Modern malware often employs sophisticated techniques such as polymorphism, metamorphism, and other evasive strategies that enable it to change its behavior dynamically and generate numerous new variants. These new malware variants predominantly represent modifications of existing threats, complicating detection efforts. As a result, there is a growing interest in leveraging machine learning algorithms (MLAs) to enhance malware analysis and detection capabilities. This research proposes a novel hybrid approach that combines advanced visualization techniques with deep learning architectures to improve the efficacy of malware detection. By employing static, dynamic, and image processing methodologies within a big data framework, this study aims to address the limitations of traditional detection methods. Specifically, we introduce a cutting-edge scalable model called ELMNet, which utilizes an extreme learning machine (ELM) classifier. This model is designed to provide robust and intelligent detection of zero-day malware, which poses a unique challenge to existing security measures. The ELMNet framework integrates various data processing techniques to enhance the accuracy and speed of malware detection. By visualizing malware characteristics and leveraging deep learning capabilities, our approach aims to achieve real-time detection and classification of emerging threats. The proposed solution not only sets a new benchmark for effective malware detection but also opens new avenues for research and practical applications in the field of cybersecurity. Ultimately, this work paves the way for more resilient and responsive malware detection systems that can adapt to the everevolving landscape of cyber threats.

Malicious software, ELMNet, Machine learning.