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"Security: Computing in an Adversarial Environment," Carrie Gates
Apr. 12, 2012: "Security: Computing in an Adversarial Environment," presented by Carrie Gates, CA Labs; moderated by Christopher W. Clifton, Purdue University. Security is inherently different from other aspects of computing due to the presence of an adversary. As a result, identifying and addressing security vulnerabilities requires a different mindset from traditional engineering. Proper security engineering—or the lack of it!—affects everything from website scripts to supply chain management to electronic health records to social networks to mobile phones...and the list goes on. Security is further complicated by the translation of social notions—such as identity and trust— into an online world. Worse, security itself is often viewed by both developers and users as the adversary! This learning webinar will introduce the fundamentals of security, describe the security mindset, and highlight why achieving security is difficult. What you'll learn: The security mindset what it is, why it's needed The social side of security usability, adoption, identity, trust A deeper dive on insider threat as a case study what it is, how to detect it, how to prevent it Presenter: Carrie Gates, Senior Vice President and Director of Research, CA Labs Dr. Gates has opened new avenues for collaboration in the field of cyber security for CA Technologies by leveraging government programs that further research between CA Labs and academia. She has given over 20 invited talks internationally, authored more than 40 peer-reviewed publications related to information security, and co-authored an amendment on cloud security research for the America Competes Act that was signed into law in December 2010. In October 2010, Dr. Gates was recognized for her work with a Women of Influence award from CSO magazine. Moderator: Christopher W. Clifton, Associate Professor of Computer Science, Purdue University Dr. Clifton works on data privacy, particularly with respect to analysis of private data.
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Deep Learning For Realtime Malware Detection - Domenic Puzio and Kate Highnam
Domain generation algorithm (DGA) malware makes callouts to unique web addresses to avoid detection by static rules engines. To counter this type of malware, we created an ensemble model that analyzes domains and evaluates if they were generated by a machine and thus potentially malicious. The ensemble consists of two deep learning models – a convolutional neural network and a long short-term memory network, both which were built using Keras and Tensorflow. These deep networks are flexible enough to learn complex patterns and do not require manual feature engineering. Deep learning models are also very difficult for malicious actors to reverse engineer, which makes them an ideal fit for cyber security use cases. The last piece of the ensemble is a natural-language processing model to assess whether the words in the domain make sense together. These three models are able to capture the structure and content of a domain, determining whether or not it comes from DGA malware with very high accuracy. These models have already been used to catch malware that vendor tools did not detect. Our system analyzes enterprise-scale network traffic in real time, renders predictions, and raises alerts for cyber security analysts to evaluate. Domenic Puzio is a Data Engineer with Capital One. He graduated from the University of Virginia with degrees in Mathematics and Computer Science. On his current project he is a core developer of a custom platform for ingesting, processing, and analyzing Capital One’s cyber-security data sources. Built entirely from opensource tools (NiFi, Kafka, Storm, Elasticsearch, Kibana), this framework processes hundreds of millions of events per hour. Currently, his focus is on the creation and productionization of machine learning models that provide enrichment to the data being streamed through the system. He is a contributor to two Apache projects. Kate Highnam has a background in Computer Science and Business, focusing on security, embedded devices, and accounting. At the University of Virginia, her thesis was a published industrial research paper containing an attack scenario and repair algorithm for drones deployed on missions with limited ground control contact. After joining Capital One as a Data Engineer, Kate has developed features within an internal DevOps Pipeline and Data Lake governance system. Currently, she builds machine learning models to assist cybersecurity experts and enhance defenses.
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