What Is Inline Deep Learning?

3 min. read

Artificial Intelligence (AI) is being increasingly used across multiple industries to fuel all sorts of automated tasks, and two large components of AI are machine learning and deep learning. Although these two are often used interchangeably, there are distinct differences that make deep learning more advanced and beneficial.

What is Deep Learning?

Deep learning is a subset of machine learning (ML) that uses artificial neural networks - algorithms modeled to work like the human brain - to mimic the functionality of the brain and learn from large amounts of unstructured data. It is extremely beneficial to data scientists, making the process of collecting, analyzing and interpreting data much faster and easier, and is a critical component of automating the traditional technique of predictive analytics. Deep learning can be especially helpful when inspecting large amounts of real-world cyberthreat data in order to detect and avoid cyberattacks.

How Does Deep Learning Work?

Deep learning is powered by neural networks, which are inspired by the biological network of neurons in humans, to emulate the behavior of the human brain. These multilayered, deep neural networks are trained using large amounts of unstructured data and can take in and analyze information from multiple data sources in real time, without any human intervention. Deep learning automates feature extractions, removing any dependency on humans. For example, when trying to categorize animals, such as dogs, cats, or birds, deep learning will determine which features (e.g., ears, nose, eyes, etc.) are critical to distinguishing each animal from another. These advanced capabilities are what make deep learning extremely beneficial in improving many analytical and automation-related tasks.

Machine Learning vs Deep Learning

Both machine learning and deep learning fall under the category of AI and function in similar ways. While deep learning is technically a subset of machine learning, it's almost more of an evolution. Still, there are some key differences in their capabilities.

Before we make those distinctions, we first need to define machine learning. Machine learning is an application of AI that includes algorithms which parse data, learn from the datasets, and then apply these learnings to make informed decisions. Typically, computers are fed structured data and use this as training data to become better at evaluating and acting. While basic machine learning models are designed to improve their accuracy of decision-making over time, they still require human intervention.

This is the first key difference between the two. Machine learning requires a data scientist or engineer to manually choose features or classifiers, check if the output is as required, and adjust the algorithm if the predictions generated are deemed inaccurate.

Deep learning removes the need for human intervention. Structuring algorithms into layers through its neural networks, Deep Learning is able to determine on its own if a prediction is accurate or not.

The second key difference is that machine learning algorithms tend to have a simple architecture, such as linear regression or a decision tree. Machine learning also tends to involve less processing power since it is less complex and can run on conventional computers. This also means that it can be set up and operated rather quickly but may yield limited results. Deep learning is far more complex in its nature, using multilayer artificial neural networks. While it does typically require more powerful hardware, resources and setup time, it often generates results instantaneously and requires minimal, if any, upkeep over time.

The third distinction between the two is in the amount of data required. Traditional machine learning algorithms require much less data than deep learning models. Machine learning can operate using thousands of data points, while deep learning typically requires millions. The data used is also largely unstructured, and can include images and videos, allowing it to eliminate fluctuations and make high-quality interpretations.

What is Inline Deep Learning?

As mentioned above, deep learning is used in a wide array of industries, including cybersecurity. Because it is able to continually evolve and learn over time from the volumes of threat data it ingests, it has become a key technology for trying to predict cyberattacks. To further its effectiveness in detecting and preventing new and never-before-seen cyberthreats, some organizations have started using inline deep learning. Inline deep learning is the process of taking the analysis capabilities of deep learning and placing it inline. For example, in the event of a security breach, inline deep learning is used to analyze and detect malicious traffic as it enters a network, and block threats in real time. This is especially crucial due to modern threat actors using sophisticated techniques that make their attacks unknown to traditional security defenses.

Inline deep learning comprises three main components, which make it well equipped against modern cyberthreats:

  • Threat detection capabilities trained by a large volume of real-world threat data.
  • Analysis is done inline to inspect real-world traffic as it enters the network.
  • Massive processing power for deep learning analysis and real-time verdicts and enforcement.

While Inline Deep Learning has these incredible capabilities, it also operates without disrupting an individual’s ability to use their device. It runs in the background unnoticed, causing no disruptions to the device’s workflow or productivity.

To learn more about Inline Deep Learning, read Palo Alto Networks’ whitepaper: Requirements for Preventing Evasive Threats.