Examining payload content is an important aspect of network security, particularly in today\u27s volatile computing environment. An Intrusion Detection System (IDS) that simply analyzes packet header information cannot adequately secure a network from malicious attacks. The alternative is to perform deep-packet analysis using n-gram language parsing and neural network technology. Self Organizing Map (SOM), PAYL over Self-Organizing Maps for Intrusion Detection (POSEIDON), Anomalous Payload-based Network Intrusion Detection (PAYL), and Anagram are next-generation unsupervised payload anomaly-based IDSs. This study examines the efficacy of each system using the design-science research methodology. A collection of quantitative data and qualitative features exposes their strengths and weaknesses
