[M]aster data continues to be a critical concern for large and medium-sized enterprises…basic records on customers, products, suppliers, employees, locations, assets and more…
Unfortunately, in most organizations, this operational information is duplicated and scattered across multiple systems and applications. As it evolves independently, it becomes error-prone and keeps decision makers from having a unified view of operational intelligence. The disparate information also prevents customers from getting the accurate and timely information they need to make purchasing decisions.
For a decade now, many Network Forensics Analysts, Network Security Engineers, and Cyber security Professionals have pondered that most interesting of questions: What do “they” know about my network? From time to time over the years, discussions related to determining what external entities may know about determining the attack surface of a network occur and then fizzle out. Often, organizations collect and store a great deal of data to piece together a defensive view of a network but do not piece together what external entities know about or have shown interest in on the same network. Big Data offers the potential to evaluate this question in ways that were unimaginable just five years ago. New technologies and techniques enable organizations to evaluate the question of what is the known attack surface of my network. I addressed this question head-on using a variety of cyber security data sets, enrichment techniques, Cloudera CDH 4 (Hadoop distribution), and Platfora: a relative newcomer that is one of the most powerful tools I have worked with in some time.
In the spirit of this International Year of Statistics, the McDonald’s analytical approach speaks to some of the fundamentals of the field. It is a great example of how statistics can dramatically affect a mega-corporation