Abstract: A system for autonomous cybersecurity probing includes a scanning module adapted to convert a target computing device or network scan to machine readable form. The scanning module includes an ingest module which processes the scan to create nodes representing the target ports, port status, and vulnerabilities. A command module includes a plurality of nodes representing facts, rules, actions, and verifiers associated with one or more vulnerabilities identified by the scanning module. The command module is configured to determine whether to launch an attack. An attack module is configured to, on receipt of instructions from the command module, assign an attack based on a one of the one or more vulnerabilities. A verifier module is configured to determine success or failure of the assigned attack and to return an indicator of the determined success or failure to the command module. Methods for cybersecurity probing using the described system are provided.

Abstract: Using a distributed set of unsupervised workers to produce a work product is disclosed. In some embodiments, a work product is received. A review task to review the work product is provided to a reviewing worker included in the set of unsupervised workers. A result of the review task is received. A determination is made, based at least in part on the review result, whether the work product satisfies an acceptance criteria.

Abstract: A reputation system to evaluate work is disclosed. Answer data for each of one or more unsupervised workers is received. The answer data includes one or more answers each representing a judgment by the worker that reflects on the quality of a given work product. The reputation system determines programmatically, based at least in part on a reputation data of an owner of the work product, the answer data, and a respective reputation data of the respective unsupervised workers, which answers are correct.

Abstract: A method and apparatus to provide an improved statistical model is disclosed. In one embodiment a statistical model for an electronic communication media is generated. The statistical model based on a predetermined set of features of the electronic communication. The statistical model is thereafter processed with a genetic algorithm (GA) to generate a revised statistical mode. In one embodiment, the revised statistical model is provided in a classifier to classify incoming electronic communications. In one embodiment, the classifier is to determine whether a received electronic communication is to be classified as spam or legitimate.

Abstract: A method and apparatus to use a statistical model to classify electronic communications is disclosed. In one embodiment, an incoming electronic communication is analyzed in view of a preformulated statistical model to determine whether the communication is to be classified within at least one predetermined category. In one embodiment, the statistical model includes a set of features relating to an electronic communication.