Cyber systems, including the Internet of Things (IoT), are increasingly being used ubiquitously to vastly improve operational efficiencies and reduce costs in critical areas, such as finance, transportation, defense, and healthcare. Over the past two decades, dramatic improvements in computing efficiencies and hardware costs have made most of our today’s economy increasingly ever more digitized. It is important to note that such widespread use of devices for providing various services has resulted in the generation of large amounts of rich user data which needs to be protected. Emerging trends in successful targeted cyber system breaches have shown increasing sophistication, with most of them using intelligence generated through the collection and integration of publicly available data. Such sophisticated attacks can only be thwarted by defense mechanisms that rely on specific actionable intelligence. Although it is true that more data from diverse sources are available, such data may not automatically translate to actionable intelligence. In fact, translating large quantities of such diverse datasets into actionable intelligence is a nontrivial process. It involves identifying and integrating useful pieces of information from large quantities of noisy and biased datasets. In this talk, we will discuss some useful deep learning techniques and various challenges in generating actionable pieces of intelligence utilized for thwarting such sophisticated targeted attacks.