Abstract: Cyberthreats are becoming smarter and more complex as the threat players have access to newer, AI driven, cloud enhanced technologies. Having the ability to forecast cyberattacks before they happen depends on the same AI capabilities. How can we outsmart the cyberthreats? The answer is by augmenting traditional Cybersecurity with the help of Machine Learning and Deep Leaning technologies. This session will be focused on defining the threat players, types of security threats and defensive techniques, and will culminate with a practical example on how Generative Adversarial Networks can help build proactive, resilient, and robust cybersecurity solutions.
Bio: Serjesh Sharma is seasoned data analytics and machine learning professional with rich experience in data science, machine learning, cloud managed services and MLOPS. He has successfully executed many machine learning projects related to NLP, chatbot, ML pipeline standardization and automation, feature stores and traditional data science use cases for fortune 500 companies. He previously held roles in data engineering (Big Data and ETL) and full stack development both as an individual contributor and tech lead at various point in his career. He has a graduate degree in analytics and certifications in AWS, Azure, and Kubernetes technologies.