Simple implementation and autonomous operation features make the Internet-of-Things (IoT) vulnerable to malware attacks. Static analysis of IoT malware executable files is a feasible approach to understanding the behavior of IoT malware for mitigation and prevention. However, current analytic approaches based on opcodes or call graphs typically do not work well with diversity in central processing unit (CPU) architectures and are often resource intensive. In this paper, we propose an efficient method for leveraging machine learning methods to detect and classify IoT malware programs. We show that reliable and efficient detection and classification can be achieved by exploring the essential discriminating information stored in the byte sequences at the entry points of executable programs. We demonstrate the performance of the proposed method using a large-scale dataset consisting of 111K benignware and 111K malware programs from seven CPU architectures. The proposed method achieves near optimal generalization performance for malware detection (99.96% accuracy) and for malware family classification (98.47% accuracy). Moreover, when CPU architecture information is considered in learning, the proposed method combined with support vector machine classifiers can yield even higher generalization performance using fewer bytes from the executable files. The findings in this paper are promising for implementing light-weight malware protection on IoT devices with limited resources.