Recent developments in artificial intelligence (AI) have led to a new technology called deep learning. Developed by computer scientists around ten years ago, this system is now being used across industries to solve difficult problems. In fact, it has become one of the most prevalent technologies for use in AI systems.
Deep learning involves creating an algorithm that learns complex patterns from data using specific neural networks. This pattern-matching ability allows computers to analyze large amounts of information quickly and efficiently, making it possible to process vast quantities of data within seconds or minutes.
Because these algorithms are structured similarly to the neurons in our brains, experts refer to them as “neural network models” or simply “networks.” They say that you can think about net cars like the ones we discussed earlier in this article as analogies for how deep nets work.
Cyber professionals apply deep learning techniques in domains such as malware analysis, spam classification, threat detection, and cybersecurity risk assessment. Depending on the field, different types of data are needed to feed into the system, which makes it important to be aware of what sources contain similar levels of detail.
This article will go more in depth about some uses of deep learning applied to cybersecurity and why they matter. However, first, let’s take a look at some basics of the technology.
History of deep learning
Artificial neural networks (ANNs) are computer programs that use layers to learn information. Layers are sets of rules or instructions for manipulating data so that advanced tasks can be done.
The first layer is usually referred to as the input layer because it receives external stimuli, in this case, inputs such as images, sound, text, etc. The second layer is called the hidden layer because what it does is not intuitively obvious. This is the part of the network that processes the data and finds patterns.
The third layer is called the output layer because it produces an answer or conclusion based on the given data. In cyber security applications, these answers often relate to identifying malicious activity or behaviors.
With respect to cybersecurity threats, the most common way to apply ANNs is in detection. A detector uses past examples of known malware to teach itself how to identify more instances of malware.
Applications of deep learning
Recent developments in artificial intelligence (AI) have brought us some incredible applications. Companies are incorporating AI into their products to automate tasks so that users can focus more on other things, such as answering questions or assessing situations.
Cybersecurity is an ever-evolving field, which means there’s always something new to learn. Technologies like computer vision and natural language processing (NLP) have made it possible for machines to perform complex analysis beyond just looking at pictures or reading text.
Neural networks are one type of machine learning algorithm that use connections between nodes to simulate how humans process information. By having large numbers of interconnected neurons, they can come up with the most efficient way to combine input data to produce an answer.
Deep neural networks are special cases of neural networks because they contain multiple layers that communicate directly with each other. This allows them to not only identify patterns in larger sets of data, but also find correlations that may go unnoticed otherwise.
There are several reasons why technology companies choose to implement these algorithms into their software. Computational power has become very accessible, making it possible to train neural network systems using vast amounts of data.
The second reason is that many of these systems are said to be able to solve problems intuitively, without requiring too much human intervention.
Challenges of deep learning
Recent developments in artificial intelligence (AI) have ushered in an era of so-called “deep learning.” Developed in the 1980s, AI that uses this approach is called neural networks because it mimics how neurons work in our brains.
By using many simple computational units to perform complex tasks, deep learners are powerful tools for computer scientists. They can also be quite tricky to use though!
That’s why there are only a few companies with large internal data sets trained as deep learners. Businesses must either hire experts to do it themselves or outsource it to a company that has already mastered the tool.
Cyber security professionals may not know how to implement these algorithms yet, which is one of the main reasons they aren’t used more. It takes time to learn this technique, and most people don’t have the resources available to do so.
Deep learning in cybersecurity
Recent developments in artificial intelligence (AI) have ushered in an era of so-called “deep learning”. This is not your average AI technology, however!
Deep learning does not use preprogrammed rules to determine if something is an image or text of a sentence, for example. Rather, it trains software using large datasets that it learns from.
The software then applies this knowledge to new data to ascertain what information should be understood. For instance, deep learning algorithms look at lots of images and textures and patterns to recognize other pictures.
Cybercriminals are increasingly employing advanced tools like deep learning to disguise malicious activity as innocuous content and processes. By disguising these threats, normal security measures often fail to pick up on them.
That’s why it’s important for cybersecurity professionals to stay abreast of such techniques. Here, we will discuss some applications of deep learning in cybersecurity, and how you can prepare.
Tips for using deep learning in cybersecurity
Recent developments in computer science have led to another revolution in technology: neural networks. A lot of applications of neural networks are referred to as “deep learning,” which is an appropriate term given how powerful they can be.
Deep learning applies concepts from neuroscience to make computers perform advanced tasks that require pattern recognition. For example, let’s say you wanted to recognize cats. Rather than asking your computer to look at all known examples of what a cat looks like and then check it against whatever it sees, we instead teach your computer to find patterns within patterns by having it compare two very large sets of data.
A neural network does this with neurons. Each neuron has a state (like being active or inactive) and connections to other neurons. The way these connections work is determined during training, which means the system already has some understanding of what a cat is due to someone teaching it before.
When you give a computer a task it hasn’t seen done yet, it starts working on figuring out how to apply those lessons to accomplish that new thing. This is where neural networks get tricky because there isn’t any hard and fast rulebook for creating them!
There are many strategies for initializing, or starting, a neural net, so it’s important to do research into different ones and see if one seems to work better than the others for your application.
Look at the context
Recent developments in computer science, such as deep learning, have come into play for cybersecurity professionals. Systems using this technology are sometimes referred to as “neural networks.”
These systems work by having large numbers of interconnected layers that process input data sequentially. Each layer takes the output from the previous one and uses it as an input for the next set.
The first few layers use simple mathematical functions to process information but later ones develop more complex relationships to achieve their goal. For example, they may look at three images of the same person and determine if each picture is of someone doing something good (like smiling) or bad (like pointing a gun). The neural network then determines which of those two it thinks is most likely true for this new image.
Deep learning has been applied to various tasks including natural language processing, speech recognition, and now cybersecurity.
Test your knowledge
Recent developments in computer science are referred to as neural networks or deep learning. Neural networks are inspired by how neurons work in our brains!
In fact, some studies have suggested that we use something similar to this system when we learn new things. When you teach someone a new skill like playing the guitar, you can start with what’s called direct teaching.
Direct teaching is basically just telling them directly how to do it. For example, if they asked you how to play the piano, you could show them every note and the timing of each one and so on until you got through all the steps.
But most people don’t know how to play the guitar already, so those who want to pick up music usually go through indirect teaching instead. This uses concepts and examples for lessons first before moving onto more detailed information.
Cybersecurity professionals now use deep learning to perform tasks such as detecting malware, phishing scams, and other types of malicious activity. Technology companies develop these applications independently and then make them available to others to use.
There are even competitions where individuals and teams get points based on their performance on predefined cybersecurity datasets. These sets contain enough data to practice using the application software given, but no real-world instances of attacks.
Team members working together collaboratively come up with different strategies to see which one works best on the test set.
In cybersecurity, detecting malicious activity is one of the most important things you can do to prevent harm to your organization or website. With such an ever-growing number of potential cyberattacks, there are now vast amounts of data available for analysts to look into.
Deep learning has become a powerful tool for analyzing this large amount of data. By using AI technology, computer programs can learn complex patterns across large datasets that describe what types of activities and behaviors are similar to something else that’s suspicious.
These similarities help identify whether certain behavior is out of place, suggestive of potentially harmful actions. If a pattern emerges that indicates malicious intent, then it can be investigated more closely to determine if someone committed a crime or not.
Threat intelligence (TI) professionals use deep learning to analyze malware samples, threat intel reports, and other pieces of information gathered through different sources. This way, they can make informed conclusions about how likely it is that another individual will commit a specific type of attack in the future.
Cyber security firms also use DL to train their algorithms to recognize new types of attacks for possible classification as “known” or “unknown.” For example, when a new phishing email message is received, the algorithm would compare it to messages known to be fraudulent to see if it looks sufficiently identical to prove guilty.