Advanced Metering Infrastructure (AMI) has evolved to measure and control energy usage in communicating through metering devices. However, the development of the AMI network brings with it security issues, including the increasingly serious risk of malware in the new emerging network. Malware is often embedded in the data payloads of legitimate metering data. It is difficult to detect malware in metering devices, which are resource constrained embedded systems, during time-critical communications. This paper describes a method in order to distinguish malwarebearing traffic and legitimate metering data using a disassembler and statistical analysis. Based on the discovered unique characteristic of each data type, the proposed method detects malicious metering data. (i.e. malwarebearing data). The analysis of data payloads is statistically performed while investigating a distribution of instructions in traffic by using a disassembler. Doing so demonstrates that the distribution of instructions in metering data is significantly different from that in malware-bearing data. The proposed approach successfully identifies the two different types of data with complete accuracy, with 0% false positives and 0% false negatives.