The significance of cybersecurity resides in its ability to safeguard individuals and enterprises against cyberattacks, as well as the unauthorized acquisition or misplacement of confidential and classified data. Cybersecurity includes the ability to employ monitoring systems to safeguard personal data, financial information, trade secrets, intellectual property, and any sensitive government information. By establishing robust cybersecurity protocols, we can enhance our ability to safeguard against a wide range of online threats. In the present age, nearly every industry is utilizing the services of diverse machine learning and deep learning models to forecast, analyze, or produce outcomes with greater optimization and efficiency. Thus, we have employed the capacity of machine learning in the task of identifying and classifying malicious software. In this paper, we propose a machine learning-envisioned malware attack detection mechanism for edge computing-based networks. The practical implementation of the proposed technique is also provided, along with the performance comparison of several existing methods for the assigned intrusion detection task. The proposed scheme achieved the highest accuracy value as compared to the other existing methods.