Click here to close now.


Java IoT Authors: Tim Hinds, Pat Romanski, Brian Daleiden, Derek Weeks, Automic Blog

Related Topics: Cloud Security, Java IoT, Microservices Expo, Linux Containers, Agile Computing, SDN Journal

Cloud Security: Article

Security Threats Continue to Grow

How Big Data and Machine Learning Can Work Together to Solve Security Threats

They read like a list of horror stories for businesses big and small alike. Sony’s PlayStation Network is hacked twice, exposing the personal information of 77 million customers. Zappos becomes the victim of a hack that exposes the addresses and phone numbers of 24 million people. Up to 81 million Yahoo email customers’ passwords are compromised, forcing the company to tell its users to reset them immediately. 110 million customers are affected when hackers infiltrate Target, and PIN numbers and credit card information are stolen. But these stories of major security breaches aren’t works of fiction--they actually happened, and it’s a concern businesses all over the world live with. Many companies are now turning to big data and machine learning as a way to tackle these risks and make sure valuable data is protected at all times.

Dealing with IT security issues is certainly nothing new for businesses. Computer viruses, malware, worms, and other threats have been around for a while, forcing companies to come up with solutions to either eliminate them or minimize the damages they cause. Much of this approach has been reactive in nature, essentially identifying a new threat or tactic hackers are using and developing the means to fight it. Older security systems had to search through smaller clusters of data to identify patterns that might indicate an attack, but the systems required significant resources and time to work, and even then their success rate was hit-and-miss. Systems were usually finding themselves being left behind by would-be attackers, forced to play catch-up in a game with a lot at stake.

With the growth of big data, data security has become even more complex and difficult to manage. More and more data is being created around the world, and trying to sort through all of it to identify security risks would tax older systems immensely. With new solutions desperately needed, many experts turned to machine learning. In simple terms, machine learning is a system that performs certain tasks by continuously learning from data without the need for specific programming. Machine learning can be used to detect security threats by sorting through all that data, something that simply wasn’t possible to that extent several years ago. Unlike traditional systems, which can get bogged down the more data they have to sort through, machine learning can actually get better if more data is added.

The way machine learning is able to detect security threats is by going through the data and identifying the signs and code that point to potential risks. This in turn creates a profile of what to look for, allowing machine learning and security systems to be able to predict and act on threats before they even happen. Essentially, machine learning can be used for security in much the same way it is used for advertising and marketing, targeting certain features it has determined through pattern recognition and using behavioral analytics to make more accurate predictions. This analysis is not only able to capture the hard data involved in security risks, it captures the context of risky events and can connect the relationships of those events to better understand just how threatening the risk actually is. This entire process takes less time than traditional systems and does not slow down productivity.

Threat detection through machine learning and big data was once out of reach for smaller businesses due to cost concerns and personnel requirements, but as these technologies have matured, smaller operations are now getting more access through big data cloud technology. The advances in recent years makes the utilization of machine learning possible for smaller security teams. In fact, security threat detection through machine learning is more of a hands-off process since machine learning systems undergo training on their own. The system is always learning, populating training sets to always get better at detecting security risks, even if they are new. The processing power and storage capabilities needed for machine learning are also within reach for small businesses thanks to advances in flash storage. The growing adaptability for companies makes security more robust and predictive instead of reactive.

There will never be a way to completely eliminate all security threats. Hackers and malware artists will always be looking for news ways to infiltrate and steal corporate information. But with a better understanding of the ways big data and machine learning can work together toward addressing this common problem, security breaches will be rarer and not as painful as those that have happened in recent years. A more secure future is definitely possible through machine learning.

More Stories By Gil Allouche

Gil Allouche is the Vice President of Marketing at Qubole. Most recently Sr. Director of Marketing for Karmasphere, a leading Big Data Analytics company offering SQL access to Apache Hadoop, where he managed all marketing functions, Gil brings a keen understanding of the Big Data target market and its technologies and buyers. Prior to Karmasphere, Gil was a product marketing manager and general manager for the TIBCO Silver Spotfire SaaS offering where he developed and executed go-to-market plans that increased growth by 600 percent in just 18 months. Gil also co-founded 1Yell, a social media ad network company. Gil began his marketing career as a product strategist at SAP while earning his MBA at Babson College and is a former software engineer.

@ThingsExpo Stories
Today air travel is a minefield of delays, hassles and customer disappointment. Airlines struggle to revitalize the experience. GE and M2Mi will demonstrate practical examples of how IoT solutions are helping airlines bring back personalization, reduce trip time and improve reliability. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Dr. Sarah Cooper, M2Mi’s VP Business Development and Engineering, explored the IoT cloud-based platform technologies driving this change including privacy controls, data transparency and integration of real time context with p...
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, demonstrated examples of com...
As organizations realize the scope of the Internet of Things, gaining key insights from Big Data, through the use of advanced analytics, becomes crucial. However, IoT also creates the need for petabyte scale storage of data from millions of devices. A new type of Storage is required which seamlessly integrates robust data analytics with massive scale. These storage systems will act as “smart systems” provide in-place analytics that speed discovery and enable businesses to quickly derive meaningful and actionable insights. In his session at @ThingsExpo, Paul Turner, Chief Marketing Officer at...
In his keynote at @ThingsExpo, Chris Matthieu, Director of IoT Engineering at Citrix and co-founder and CTO of Octoblu, focused on building an IoT platform and company. He provided a behind-the-scenes look at Octoblu’s platform, business, and pivots along the way (including the Citrix acquisition of Octoblu).
In his General Session at 17th Cloud Expo, Bruce Swann, Senior Product Marketing Manager for Adobe Campaign, explored the key ingredients of cross-channel marketing in a digital world. Learn how the Adobe Marketing Cloud can help marketers embrace opportunities for personalized, relevant and real-time customer engagement across offline (direct mail, point of sale, call center) and digital (email, website, SMS, mobile apps, social networks, connected objects).
The Internet of Everything is re-shaping technology trends–moving away from “request/response” architecture to an “always-on” Streaming Web where data is in constant motion and secure, reliable communication is an absolute necessity. As more and more THINGS go online, the challenges that developers will need to address will only increase exponentially. In his session at @ThingsExpo, Todd Greene, Founder & CEO of PubNub, exploreed the current state of IoT connectivity and review key trends and technology requirements that will drive the Internet of Things from hype to reality.
Two weeks ago (November 3-5), I attended the Cloud Expo Silicon Valley as a speaker, where I presented on the security and privacy due diligence requirements for cloud solutions. Cloud security is a topical issue for every CIO, CISO, and technology buyer. Decision-makers are always looking for insights on how to mitigate the security risks of implementing and using cloud solutions. Based on the presentation topics covered at the conference, as well as the general discussions heard between sessions, I wanted to share some of my observations on emerging trends. As cyber security serves as a fou...
We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in his General Session at 17th Cloud Expo, Scott Cleland, Senior Director of Product Marketing at HGST, showed how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources.
With all the incredible momentum behind the Internet of Things (IoT) industry, it is easy to forget that not a single CEO wakes up and wonders if “my IoT is broken.” What they wonder is if they are making the right decisions to do all they can to increase revenue, decrease costs, and improve customer experience – effectively the same challenges they have always had in growing their business. The exciting thing about the IoT industry is now these decisions can be better, faster, and smarter. Now all corporate assets – people, objects, and spaces – can share information about themselves and thei...
The cloud. Like a comic book superhero, there seems to be no problem it can’t fix or cost it can’t slash. Yet making the transition is not always easy and production environments are still largely on premise. Taking some practical and sensible steps to reduce risk can also help provide a basis for a successful cloud transition. A plethora of surveys from the likes of IDG and Gartner show that more than 70 percent of enterprises have deployed at least one or more cloud application or workload. Yet a closer inspection at the data reveals less than half of these cloud projects involve production...
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem" in this scenario: microservice A (releases daily) depends on a couple of additions to backend B (re...
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Container technology is shaping the future of DevOps and it’s also changing the way organizations think about application development. With the rise of mobile applications in the enterprise, businesses are abandoning year-long development cycles and embracing technologies that enable rapid development and continuous deployment of apps. In his session at DevOps Summit, Kurt Collins, Developer Evangelist at, examined how Docker has evolved into a highly effective tool for application delivery by allowing increasingly popular Mobile Backend-as-a-Service (mBaaS) platforms to quickly crea...
Growth hacking is common for startups to make unheard-of progress in building their business. Career Hacks can help Geek Girls and those who support them (yes, that's you too, Dad!) to excel in this typically male-dominated world. Get ready to learn the facts: Is there a bias against women in the tech / developer communities? Why are women 50% of the workforce, but hold only 24% of the STEM or IT positions? Some beginnings of what to do about it! In her Day 2 Keynote at 17th Cloud Expo, Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, wil...
PubNub has announced the release of BLOCKS, a set of customizable microservices that give developers a simple way to add code and deploy features for realtime apps.PubNub BLOCKS executes business logic directly on the data streaming through PubNub’s network without splitting it off to an intermediary server controlled by the customer. This revolutionary approach streamlines app development, reduces endpoint-to-endpoint latency, and allows apps to better leverage the enormous scalability of PubNub’s Data Stream Network.