In this code story, we will discuss applications of Hierarchical Attention Neural Networks for sequence classification. In particular, we will use our work the domain of malware detection and classification as a sample application. Malware, or malicious software, refers to harmful computer programs such as viruses, ransomware, spyware, adware, and others that are usually unintentionally installed and executed. When detecting malware in a running process, a typical sequence to analyze could be a set of disk access actions that the program has taken. To analyze software without running it, we can treat series of assembly instructions in the disassembled binary as sequences to be classified in order to identify sections of the code with malicious intent. In our novel approach, we apply techniques typically used to discover structure in a narrative text to the data that describes the behavior of executables.