Abstract: A cybersecurity service assesses, scores, and/or prioritizes activities associated with a directory service. When the directory service is requested to change a directory service assignment, the directory service may first request a verdict from the cybersecurity service. The cybersecurity service may use profiling and/or machine learning to predict directory service assignments. The cybersecurity service may then score and prioritize requests to change/update directory service assignments. Small deviations from predicted directory service assignments, for example, may indicate harmless/normal directory service activity. Larger deviations, though, may indicate abnormal directory service activity. Larger deviations may even indicate malicious directory service activity, such as permission escalation and cyberbreaches. Scoring and prioritization allows for resource allocation and timely mitigations by human experts.

Abstract: Automated source code similarity greatly improves computer functioning. Any source code file is evaluated with respect to publicly-available open source code. If the source code file is similar to the publicly-available open source code, then a computer system may be approved or authorized to perform any hardware/software operations associated with the source code file. Should, however, the source code file be dissimilar to the publicly-available open source code, then the hardware/software operations are blocked to prevent disclosure of the source code file. For example, read/write/input/output operations are blocked and/or network interfaces are disabled. Source code similarity thus thwarts suspicious activities that indicate misappropriation or exfiltration of the source code file.

Abstract: Embedding entity matching greatly improves computer functioning. Different datasets are matched to a common entity using entity embeddings generated by a machine learning entity embedding model. The entity embeddings are converted to entity similarities, thus revealing the datasets associated with the common entity. Efficient matrix operations further improve computer functioning. Embedding entity matching thus quickly identifies common employee records and user accounts using less hardware resources, less electricity, and less time.

Abstract: An influence maximization process efficiently identifies an influential set of nodes with which to seed a diffusion process using the transposition of a graph representing the network. This approach offers an acceptable tradeoff between runtime complexity and accurate approximation. In addition, using an approximation condition, the influence maximization process may be further tuned to dramatically reduce the computational complexity even more in certain circumstances while allowing a fallback to the unturned influence maximization process if the approximation condition is not satisfied.

Abstract: An influence maximization process efficiently identifies an influential set of nodes with which to seed a diffusion process using the transposition of a graph representing the network. This approach offers an acceptable tradeoff between runtime complexity and accurate approximation. In addition, using an approximation condition, the influence maximization process may be further tuned to dramatically reduce the computational complexity even more in certain circumstances while allowing a fallback to the unturned influence maximization process if the approximation condition is not satisfied.