Information assurance, also referred to as cyber security, is the process of protecting information from theft, destruction, or manipulation. Cyber threats can be either from internal or external sources, sudden or taking time to develop, such as a slow denial of service (DOS) attack. Some techniques have been developed to behave as sensors to quickly assess elements of attacks that rely on a decision engine to fuse the information to estimate whether or not an attack is underway. Interpreting cybersecurity as a sensor fusion problem, includes a number of additional alternative techniques into the solution space. The concept of evidence accrual is to gather measurements over time from different sensors to provide estimates of what event is occurring. A classification fusion technique using feature extraction and fuzzy logic known as Feature Object Extraction is developed and applied to problems such as cyber security and GPS attacks. The feature-aided object extraction technique was developed for the classification problem to fuse different features and generate both a classification and a measure of the quality of the classification estimate. A primary advantage of this is that it evidence is built for each possibility without excluding classes. Thus, the evidence may point to multiple possibilities until evidence disproves a class. Most probabilistic techniques increase the probability of one class by lowering the probability on other classes. Another difference exists in the fact that evidence can be applied to individual classes and not all classes. Feature Object Extraction also allows for a level of evidence to recover from erroneous negative information which might normally cause elimination of a possibility. These design features of Feature Object Extraction are applied to the cybersecurity problem where multiple attacks might be underway simultaneously.