In my role as leader of the Analytics Practice for Cisco® Consulting Services, I often meet with clients who remind me of how the nature of consulting is changing. Traditionally, a consultant’s value and relevance to the customer has been derived from his or her business background and knowledge of specific industries or areas of expertise. The consultant comes in and takes a look at the client’s critical business issues, then makes top-down recommendations based on his or her specialized business experience.
This traditional model is being challenged by what I call “digital disruptors”—consultants whose credibility comes not just from their past experience, but from their ability to extract value and insight based on data that is gathered at the operational base of the organization: the network. This bottom-up approach is turning the consulting industry on its head—driven by data gathered on the network and turned into business insights by analytics.
Consider, for example, a major enterprise that has made a large investment in infrastructure for video collaboration. The company’s leaders want to see what kind of value they are getting back from their investment in order to evaluate further investment in collaboration. Cisco Consulting can help this customer not only because of our industry expertise, or even because of our knowledge of video collaboration technology—but because we can take an analytics-based, digital-disruption approach to the customer’s challenges. The key is our ability to tap into the video infrastructure itself, combine network and other types of data, and give the client a view of how the infrastructure is being utilized.
The success of our industry and even our nation’s well-being are dependent on engaging students and developing the experts of the future in the areas of science, technology, engineering and mathematics (STEM). I am personally committed to STEM education initiatives, and want to share an exciting university that’s breaking new ground to lead the way and ensure students have a clear path to STEM careers. As the newest member of the State University System of Florida, Florida Polytechnic University is dedicated exclusively to STEM.
Within their College of Engineering and the College of Innovation and Technology, Florida Poly will offer six undergraduate degree programs and two Master degree programs. These include some really unique areas of concentration including Big Data Analytics, Cloud Virtualization, Health Informatics, Cyber Gaming, Information Assurance and Cyber Security, and even more.
Another unique aspect of this high-tech university is that they work closely with industry partners to ensure strong relevance to real-world needs. This will ensure graduates are learning the critical skills needed to join some very competitive workforces. In fact, all you have to do is check out the campus to be impressed:
If you are in Florida, check out the PolyPremiere – a campaign where Florida Poly is rolling out the purple carpet at movie theaters across the state to give potential students an in-depth look at Florida Poly’s campus, curriculum, culture and scholarships.
Where are your tech students looking for STEM degree opportunities?
In the last chapter of our five part Big Data in Security series, expert Data Scientists Brennan Evans and Mahdi Namazifar join me to discuss their work on a cloud anti-phishing solution.
Phishing is a well-known historical threat. Essentially, it’s social engineering via email and it continues to be effective and potent. What is TRAC currently doing in this space to protect Cisco customers?
Brennan: One of the ways that we have traditionally confronted this threat is through third-party intelligence in the form of data feeds. The problem is that these social engineering attacks have a high time dependency. If we solely rely on feeds, we risk delivering data to our customers that may be stale so that solution isn’t terribly attractive. This complicates another issue with common approaches with a lot of the data sources out there: many attempt to enumerate the solution by listing compromised hosts and in practice each vendor seems to see just a small slice of the problem space, and as I just said, oftentimes it’s too late.
We have invested a lot of time in looking at how to avoid the problem of essentially being an intelligence redistributor and instead look at the problem firsthand using our own rich data sources -- both external and internal - and really develop a system that is more flexible, timely, and robust in the types of attacks it can address.
Mahdi: In principle, we have designed and built prototypes around Cisco’s next generation phishing detection solution. To address the requirements for both an effective and efficient phishing detection solution, our design is based on Big Data and machine learning. The Big Data technology allows us to dig into a tremendous amount of data that we have for this problem and extract predictive signals for the phishing problem. Machine learning algorithms, on the other hand, provide the means for using the predictive signals, captured from historical data, to build mathematical models for predicting the probability of a URL or other content being phishing.
What is ARS and what problem is it trying to solve?
Dazhuo: From a high-level view, Auto Rule Scoring (ARS) is the machine learning system for our anti-spam system. The system receives a lot of email and classifies whether it’s spam or not spam. From a more detailed view, the system has hundreds of millions of sample email messages and each one is tagged with a label. ARS extracts features or rules from these messages, builds a classification model, and predicts whether new messages are spam or not spam. The more variety of spam and ham (non-spam) that we receive the better our system works.
Jisheng: ARS is also a more general large-scale supervised learning use case. Assume you have tens (or hundreds) of thousands of features and hundreds of millions (or even billions) of labeled samples, and you need them to train a classification model which can be used to classify new data in real time.
Where did graph databases originate and what problems are they trying to solve?
Michael: Disparate data types have a lot of connections between them and not just the types of connections that have been well represented in relational databases. The actual graph database technology is fairly nascent, really becoming prominent in the last decade. It’s been driven by the cheaper costs of storage and computational capacity and especially the rise of Big Data.
There have been a number of players driving development in this market, specifically research communities and businesses like Google, Facebook, and Twitter. These organizations are looking at large volumes of data with lots of inter-related attributes from multiple sources. They need to be able to view their data in a much cleaner fashion so that the people analyzing it don’t need to have in-depth knowledge of the storage technology or every particular aspect of the data. There are a number of open source and proprietary graph database solutions to address these growing needs and the field continues to grow.