|By Gil Allouche||
|May 30, 2014 10:00 AM EDT||
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.
Internet of @ThingsExpo has announced today that Chris Matthieu has been named tech chair of Internet of @ThingsExpo 2017 New York The 7th Internet of @ThingsExpo will take place on June 6-8, 2017, at the Javits Center in New York City, New York. Chris Matthieu is the co-founder and CTO of Octoblu, a revolutionary real-time IoT platform recently acquired by Citrix. Octoblu connects things, systems, people and clouds to a global mesh network allowing users to automate and control design flo...
Dec. 8, 2016 04:30 AM EST Reads: 722
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Dec. 8, 2016 02:45 AM EST Reads: 1,295
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Dec. 8, 2016 01:45 AM EST Reads: 1,406
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Dec. 8, 2016 12:30 AM EST Reads: 1,122
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Dec. 8, 2016 12:15 AM EST Reads: 1,347
What happens when the different parts of a vehicle become smarter than the vehicle itself? As we move toward the era of smart everything, hundreds of entities in a vehicle that communicate with each other, the vehicle and external systems create a need for identity orchestration so that all entities work as a conglomerate. Much like an orchestra without a conductor, without the ability to secure, control, and connect the link between a vehicle’s head unit, devices, and systems and to manage the ...
Dec. 7, 2016 10:30 PM EST Reads: 874
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Dec. 7, 2016 10:00 PM EST Reads: 1,196
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Dec. 7, 2016 08:15 PM EST Reads: 2,226
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Dec. 7, 2016 05:15 PM EST Reads: 938
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Dec. 7, 2016 05:15 PM EST Reads: 1,765
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Dec. 7, 2016 04:30 PM EST Reads: 1,684
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Dec. 7, 2016 04:15 PM EST Reads: 427
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Dec. 7, 2016 03:45 PM EST Reads: 2,274
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Dec. 7, 2016 03:30 PM EST Reads: 1,793
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Dec. 7, 2016 02:30 PM EST Reads: 950
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 7, 2016 02:30 PM EST Reads: 4,328
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Dec. 7, 2016 02:15 PM EST Reads: 2,205