![]() Since joining the EEOC, one of Commissioner Sonderling’s highest priorities is ensuring that artificial intelligence and workplace technologies are designed and deployed consistent with long-standing civil rights laws. Before joining the Department of Labor in 2017, Commissioner Sonderling practiced Labor and Employment law in Florida. Prior to his confirmation to the EEOC, Commissioner Sonderling served as the Acting and Deputy Administrator of the Wage and Hour Division at the U.S. The EEOC is the United States’ premier civil rights agency enforcing federal laws that make it illegal to discriminate against a job applicant or an employee because of the person’s race, color, religion, sex, national origin, age, disability or genetic information. ![]() Until January of 2021, he served as the Commission’s Vice-Chair. ![]() Equal Employment Opportunity Commission (EEOC) in 2020. Senate, with a bipartisan vote, to be a Commissioner on the U.S. We’ll go through the entire setup from data collection and data flushing to model building by creating weak labels and further analysis. In this talk, we will go through a real example of how a user behavior experiment was set up, right from building the features to running the data collection script within containers to flushing the raw data regularly and the users sending only aggregated metrics to the data scientists for model building and analysis. This starts off as acceptable but wades into the grey area of almost keylogging users which is dangerous. But when collecting user generated systems data from a cluster of machines in the cloud or from an endpoint, the data scientist gets access to human generated raw features, which keys are typed when, and what are those. Advancements in unsupervised machine learning methodologies have made UEBA models effective in detecting anomalous drifts from baseline behavior. User and Entity Behavior Analysis (UEBA) has been an active area of research in cybersecurity for years now. I’m not Keylogging you! Just some benign data collection for User Behavior Modeling Currently, he is researching into ways to address the cybersecurity skills gap, by utilizing machine learning to augment the capabilities of current security analysts. His work has given him the opportunity to be published in industry magazines and speak at conferences such as Def Con, Def Con China, and CactusCon. Through that passion, he runs NetSec Explained a blog and YouTube channel which covers intermediate and advanced level network security topics, in an easy to understand way. Gavin Klondike is a senior consultant and researcher who has a passion for network security, both attack and defense. ![]() No environment setup is necessary, but Python experience is strongly encouraged. In this talk, we will dive head first into building and training our own security-related models using the 7-step machine learning process. In order to properly deploy and manage these products, analysts will need to understand how the machine learning components operate to ensure they are working efficiently. However, “machine learning” is nothing more than a mysterious buzzword for many security analysts. ![]() Today, over a quarter of security products for detection have some form of machine learning built in. ![]()
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