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Deep Learning and How It’s Transforming Cybersecurity as We Know It

Deep Learning and How It’s Transforming Cybersecurity as We Know It

One of the sciences that’s going to dramatically transform our world over the next decade is artificial intelligence (AI). It’s gone from sci-fi movies to smartphone voice assistants over the last several years, helping us access information and complete tasks faster.

One of the exciting technologies to come out of the AI world is called deep learning. Deep learning represents a multi-layered artificial representation of how the human brain thinks, used for technology processes.

This type of artificial intelligence has already been in use in areas such as facial recognition and social media (trying to figure out what makes us click), but it’s now making its way into an area that can benefit just about every business owner, cybersecurity.

At Triada Networks, we specialize in providing expert IT security solutions for NYC and New Jersey financial industry businesses. We understand the importance of preventing a data breach or spyware infiltration, so we keep up on all the latest advances in data security, such as deep learning.

What does this advanced AI mean for you and your ability to keep your network safe? We’ve got a full overview of this new technology and how it’s already transforming cybersecurity.

What’s the Difference Between Deep Learning and Machine Learning?

While machine learning and deep learning may seem fairly similar, deep learning is a much more evolved form of AI. The name “deep” refers to the multi-layered way that an application can learn from new information, whereas machine learning is more singular.

Deep learning is an advanced subset of AI and is also known as “deep neural networks.” It’s meant to learn information as closely as possible to the way human brains do, and intuitively (without user input) form more mature understandings of the data provided.

To give you an example, let’s look at how an advanced GPS could work with both machine and deep learning.

Machine Learning is more one dimensional, so a GPS using it could look at the data on the map and statistical traffic patterns and other data that had been provided, such as road construction to provide you with the best route, but not necessarily be able to connect those different data points to learn anything else.

Deep Learning would have the ability to layer several areas over each other, such as the map, traffic patterns and construction data, but also include a deeper understanding such as when school traffic was impacting zones, how drivers during rush hour drive as opposed to how they drive during non-rush hour, and use each layer of data to inform the next layer and provide a more thoughtful representation of the best route.

The key difference is that each separate layer of learned information has the ability in deep learning to inform the next layer, without any additional user input.

Other Disciplines that Use Deep Learning

Some of the other areas where the advanced knowledge capacity of deep learning is already being used include:

  • Automatic Speech Recognition (ASR)
  • Online Chatbots
  • Language Translations
  • Adding Color to Images
  • Autonomous Vehicles
  • Computer Vision for Image Recognition
  • Deep-Learning Robots

How is Deep Learning Benefitting Cybersecurity?

You might be asking why more AI is needed in cybersecurity when it seems to already be pretty advanced. Although data security capabilities do keep evolving, so do the threats, because the hackers never sleep.

A recent report from the U.S. Council of Economic Advisors estimates the total cost of malicious cyber activity to the U.S. economy in 2016 between $57 billion and $109 billon. This illustrates the importance of pulling in as many technological resources as we can to fight it.

Imagine a data security system that could not only watch for threats that were programmed into the software but was able to learn and evolve on its own. It could use the patterns of both your users and the hackers in build its knowledgebase and intuitively estimate their next move to attack your network and take steps to protect you before it happens.

In deep learning, as more data is provided, the application gets better at intuitively understanding the meaning of that data, without human assistance. (DeepInstinct)

DeepInstinct is noted as the first company to apply deep learning to cybersecurity and here’s an overview of how this works.

Data Training

Any smart AI system needs information in the form of data to learn from. Data training is done regularly in accordance with market analysis and any new threats and cyberattack trends. Data samples are used from:

  • Public repositories
  • Darknet
  • Homegrown malware
  • Malware mutations

Deep Learning

The deep neural network algorithms are specially engineered for cybersecurity use and begin formulating defenses to data security threats based upon the multiple layers of data received.

Prediction

The software includes a light-weight agent that can be installed on all of an organization’s endpoints (computers, servers, mobile devices), that provides real-time pre-execution of cybersecurity protection protocols.

Is Your Business Network Safe from Malicious Activity?

The cost of data breaches is high for business across the country and especially so for those in the financial services industry.

Don’t leave your business exposed, contact Triada Networks today to schedule a free consultation and find out exactly how well you’re protected.