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Mobility Trends in the Air Industry 2014


Mobile usage and penetration within the Air Industry is an interesting topic that most of us can relate to and understand to one degree or another. Having attended the recent Air Passenger EXPO in Barcelona, some of the published reports make for interesting reading. Here are some interesting perspectives and trends:

Airline Perspective:

  • During the next 3 years, 98% of Airlines are investing in  delivering Passenger Services via Mobile devices
  • With 97% investing in Personalization of services to customers
  • And 94% planning on delivering Customer Services via social media.

Wireless penetration onboard is growing.

  • with 55% of Airlines planning investing in wireless for passengers and 69% investing in wireless for crew usage. Read More »

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Mobile Trends & Airport Passenger Terminal Expo 2014

airport passenger expo 0The annual Passenger Terminal EXPO is underway this week in Barcelona. The event provides a powerful international platform for the airport and airline sectors to interact and share common issues, goals and solutions about the airport industry, with a particular and unique focus on the terminal.

Cisco’s partner SITA has a very strong presence at the event with various speakers on the industry trends as well as booth and demos. One of these is the iFlow platform which uses Cisco’s MSE as a core component.

The Airport industry is one of the most researched industries with very solid and proven metrics and clear ROI measures. Some interesting statistics recently reported include:

Airport IT Investment trends:

  • IT spending in Airports has grown by 12% CAGR since 2010,
  • with IT spend in Airports in 2013 representing 5.43% of their revenues (up from 4.9% in 2012)
  • 90% of Airport CIO’s (or technology chiefs) expect further increases in 2014 and beyond. Read More »

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Cisco CMX @ CES 2014

internationalces14This week CES was once again held in Las Vegas with in excess of 100,000 people in attendance.

Cisco demonstrated a number of CMX and IoT related things this week.

Firstly “The Internet of Everything:  On The Go”

In the Cisco booth some future thinking was applied with a concept that imagines the shopping experience with a simulated retail environment:  “BigBox.” While shopping at BigBox, visitors can walk through a combination of experiences involving location-based data, video, predictive analytics, security cameras, and sensors – designed to help retailers enrich the shopping trip for their customers, and more efficiently manage their stores.

Somewhat scary for some and exciting for others, while all the time enabling retailer increase their bottom line and deliver improved and personalized shopping experience to the consumers.

The next demo “Starlight Resort” was a combination of CMX, and Small Cell capabilities in the hotel resort environment. Read More »

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Big Data in Security – Part V: Anti-Phishing in the Cloud

TRACIn 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.


Read More »

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Big Data in Security – Part IV: Email Auto Rule Scoring on Hadoop

TRACFollowing part three of our Big Data in Security series on graph analytics, I’m joined by expert data scientists Dazhuo Li and Jisheng Wang to talk about their work in developing an intelligent anti-spam solution using modern machine learning approaches on Hadoop.

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.


Read More »

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