Abstract: Methods and apparatuses for improving the performance and energy efficiency of machine learning systems that generate security specific machine learning models and generate security related information using security specific machine learning models are described. A security specific machine learning model may comprise a security specific large language model (LLM). The security specific LLM may be trained and deployed to generate semantically related security information. The security specific LLM may be pretrained with a security specific data set that was generated using similarity deduplication and long line handling, and with security specific objectives, such as next log line prediction based on host, system, application, and cyber attacker behavior. The security specific large language model may be fine-tuned using a security specific similarity dataset that may be generated to align the security specific LLM to capture similarity between different security events.

Abstract: In some examples, a method of generating a security report is provided. The method includes receiving a user query and security data, and providing the user query and security data to a semantic model. The semantic model generates one or more first embeddings. The method further includes receiving, from a data model, one or more second embeddings. The data model is generated based on historical threat intelligence data. The model further includes generating an execution plan based on the one or more first embeddings and the one or more second embeddings, and returning a report that corresponds to the execution plan.

Abstract: In some examples, a method for orchestrating an execution plan is provided. The method includes receiving an input embedding that is generated by a machine-learning model and receiving a plurality of stored semantic embeddings, from an embedding object memory, based on the input embedding. The plurality of stored semantic embeddings each correspond to a respective historic plan. Each historic plan includes one or more executable skills. The method further includes determining a subset of semantic embeddings from the plurality of stored semantic embeddings based on a similarity to the input embedding, and generating a new plan based on the subset of semantic embeddings and the input embedding. The new plan may be different than the historic plans that correspond to the subset of semantic embeddings. The method further includes providing the new plan as an output.

Abstract: A pair of electrodes is floated on the surface of the oil at the base of a oil well. An AC high frequency current is imposed across the gap in the electrodes to develop microfine carbon which serves as a catalyst for the electrolytic decomposition of petroleum by a plurality of elongated, nonspark-generating field electrodes which develop a high energy field at the base of the well, such elongated electrodes also being immersed in the oil at the base of the well. The developed gases, consisting primarily of hydrogen, serve to both repressurize the well and diffuse within the oil bearing strata to laterally displace sufficient oil to adjoining wells for recovery at such adjacent sites. The combination of repressurizing and by means of low molecular weight gases, effects by diffusion, this lateral displacement of petroleum in recoverable amounts to adjacent pumping sites.