Why has it become common practice to use applied data science and machine learning for cyber security?
- Hackers are using more and more sophisticated techniques, including artificial intelligence, to perform cyber attacks.
- Big data grows and changes at an increasingly rapid pace.
- Big data needs science in order to be useful.
- In today’s cyber security landscape, the “how” of an attack is just as important as the “what.”
So what does this mean for your business?
When it comes to identifying cyber security threats against your organization, the primary goal is always to discover the known patterns of malicious software. These threats can then be detected and removed, ideally before they harm your network.
Most modern cyber security tools use data science and machine learning, from your free antivirus software to more comprehensive proactive tactics. Machine learning excels at rapidly learning different patterns. Data science works in conjunction with machine learning techniques by combing through the various patterns and determining which are putting, or could put, your organization at the greatest risk.
More complex cyber security systems can utilize far more big data and machine learning algorithms than simpler tools, such as antivirus software. However, the principles behind both simpler and more advanced tools that use applied data science and machine learning for cyber security are the same.
Keep reading to learn more about what these terms means, why today’s tools use applied data science and machine learning for cyber security, and how these cyber security strategies work and can be implemented to protect your business.
Defining Applied Data Science and Machine Learning
Before we dive into a more in-depth discussion of how to use applied data science and machine learning for cyber security, let’s review the terminology and the relationship between these concepts.
What is machine learning?
Machine learning is a branch of artificial intelligence that refers, broadly speaking, to artificial intelligence capable of learning from provided data or its own experiences. These AI, in other words, can continually improve the accuracy of their results as they gather and analyze more data.
What is applied data science?
We live in a world abundant with data, but raw numbers alone cannot teach us anything. Data science techniques allow both machine learning tools and humans to not only discover and understand data findings, but to put those findings to practical use.
Today, all effective machine learning tools utilize applied data science. “Data science” and “applied data science” are essentially the same concept, as data science must be implemented or “applied” toward a goal or purpose in order to be useful.
How do applied data science and machine learning work together?
Most modern AI fuses machine learning strategies with applied data science. For example:
- Speech recognition apps, such as Siri, are programmed to understand human sounds. Over time, they learn to get better at recognizing commands made in your individual voice.
- Personalized recommendations, whether via a streaming service or an online shop, offer recommendations based on the content you’ve already engaged with, your browsing habits, or your shopping history.
- Epidemic trackers, such as the one our team is developing, are fed known data about a disease’s spread. Then, they attempt to predict via learned patterns where the disease might spread next.
Although you may not have realized it before, chances are high that the combined powers of data science and machine learning are already an integral part of your life. But why do we need applied data science and machine learning for cyber security?
4 Reasons to Use Applied Data Science and Machine Learning for Cyber Security
Learn more about why to use applied data science and machine learning for cyber security below! Please feel free to share our infographic on social media, or copy and paste the code below to embed it on your website:
<img src="https://bit.ly/whydatamachinelearning"> <p>4 Reasons to Use Applied Data Science and Machine Learning for Cyber Security - An infographic by the team at <a href="https://sdi.ai/">Sentient Digital, Inc.</a></p>
As mentioned earlier, contemporary cyber security tools use both machine learning and data science. However, their complexity can vary greatly. Free antivirus software, for instance, will not process nearly as much big data or have nearly as many machine learning algorithms as a comprehensive network monitoring system.
But why have most apps and tools trended toward using applied data science and machine learning for cyber security? There are 4 main reasons:
1. Hackers are using more and more sophisticated techniques, including artificial intelligence, to perform cyber attacks.
Unfortunately, not only the “good guys” can leverage machine learning and data science to their advantage. Hackers can use these same strategies to automate processes for finding the businesses and agencies most susceptible to their attacks, locating weak points within different networks, and developing new criminal technology to sidestep an organization’s existing security system. To keep our defenses competitive, we must likewise take advantage of more advanced tactics that continue to learn and improve.
2. Big data grows and changes at an increasingly rapid pace.
It may seem at first that the abundance of data out there related to cyber attacks would be beneficial to building cyber security systems. While this plethora of data is useful in some ways, the rate at which this data both multiples and transforms makes it extraordinarily difficult for the average cyber security tool—nevermind humans—to keep up. Machine learning and data science can help us sift through this data, glean insights from it, and act on it in a more timely and efficient manner.
3. Big data needs science in order to be useful.
Even after a cyber security tool has been devised that can process data at the pace that new data is being generated, that data still needs to be analyzed. For the data to actually be useful, an organization needs to know how to scientifically interpret it and apply its findings to their cyber security practices.
4. In today’s cyber security landscape, the “how” of an attack is just as important as the “what.”
Traditionally, antivirus and vulnerability scanners have focused solely on discovering what the threat to cyber security is and then eliminating that threat from the network. With modern applied data science, however, there is additional focus placed on the factors leading up to the attack, as well as the actual characteristics of the attack.
This might include analyzing the attacker’s entry point into the network, which data the attacker has access to, and where the hacker could move next within the network. Figuring out the answer to these questions about how an attack is perpetrated not only increases the likelihood of fully eliminating the hacker from your network, but also helps your company prepare better its cyber defenses for the future.
How Do Applied Data Science and Machine Learning for Cyber Security Work?
Learn more about several common ways to use applied data science and machine learning for cyber security below! Please feel free to share our infographic on social media, or copy and paste the code below to embed it on your website:
<img src="https://bit.ly/methodsdatamachinelearning"> <p> 3 Common Methods of Applied Data Science and Machine Learning for Cyber Security - An infographic by the team at <a href="https://sdi.ai/">Sentient Digital, Inc.</a></p>
Now that we have covered why an organization would find it valuable to use machine learning and data science in their cyber security system, let’s focus on how this type of cyber security works. There are many possibilities, but we’re going to focus on the most common methods and explain how you can use those methods to enhance your business’ cyber security.
Classification is a subset of supervised learning, meaning that the AI is presented with both the data input and the output. In other words, the AI is fed data points by human programmers, as well as rules about how the data points relate to one another.
Classification specifically refers to a machine learning strategy that works to predict data labels. One algorithm for classification is called a random forest classifier, which works like a decision tree.
You probably intuitively understand the basics of this algorithm. Humans often make choices using decision trees, even if we aren’t thinking of them in those terms.
As an example, before leaving your house, you would ask yourself what you needed to accomplish by leaving the house. If you just wanted a change of pace, you might go to a park or to see a friend. If you needed food, you might go to the grocery store. In that case, the series of subsequent decisions made at the store might be based on your budget, dietary restrictions, meal planning, and what you already had in the house.
Random forest classification works in the same way. The AI will model a series of data points with multiple options and try to predict which options will happen next. Instead of just one decision tree, the random forest will generate many decision trees with different variables. Data science helps to ensure that this algorithm will make predictions that are as accurate as possible.
When it comes to cyber security, classification can be helpful in a variety of scenarios:
- Labeling types of cyber attacks. Classification can be used to distinguish between spyware, ransomware, and other types of malicious software.
- Detecting different fraud techniques. Whether internet fraud, credit card fraud, or another method, this machine learning technique can help businesses figure out which type of fraud poses, or could pose, the greatest risk to them.
- Finding injection attacks. Hackers sometimes don’t install new malicious programs, but alter your existing programs instead. Classification can help detect known types of injection hacks.
Regression is another type of supervised learning. But where classification is concerned with labels, regression focuses on numerical quantities. Typically, its goal is to figure out if different factors influence one another, and if so, to what degree.
Without realizing it, you might already think in terms of regression when you’re considering buying a car, for example. A car’s price can vary greatly based on its mileage, the brand name, its size, your credit history, and numerous other factors. You might try to figure out how much the car’s price is affected by these variables ahead of time so you aren’t surprised by the cost once you’re at the dealership.
One of the most common algorithms for this type of supervised learning is simple linear regression. This algorithm tries to determine if there is a causal relationship between two variables, meaning that one of the variables directly affects the other. In the case of our car example, you could use a simple linear regression to determine to what extent a car’s price is affected by its mileage.
Regression models can help with a variety of cyber security tasks as well:
- Discovering suspicious HTTP requests. HTTP is essentially how clients and employees communicate with one another over servers. A regression model can detect if a hacker is trying to manipulate or go around these communication channels, such as through an authentication bypass.
- Comparing requested network packet parameters to their typical values. Network packets, which are measured in bytes, are how all information gets transferred online. If the byte size of one of these packets departs from its normal size, it could indicate a cyber criminal inside your network.
- Finding unexpected system calls. Endpoint security is a major concern for many offices, especially those with remote workers. Regression can help detect if an unfamiliar laptop, router, or other endpoint device is trying to access your network.
Clustering is a type of unsupervised learning. Like with supervised learning, unsupervised learning means that the AI is given data sets by humans. Unlike supervised learning, any AI taught by unsupervised learning has to teach itself the relationships between the various data points. In other words, unsupervised learning means that the machine learning system knows the data’s input, but has to discover on its own the output.
The main goal of clustering is to sort data into various groups based on which data points most resemble one another. An easy way to understand this technique is to think about a streaming service that wants to target different shows to different clients.
This streaming service likely knows some demographic information about you, such as your age and ZIP code, as well as some of the TV shows you’ve watched in the past. By clustering you into a group with other people who have similar demographics and viewing histories, the streaming service will try to make recommendations as to what you would enjoy watching next.
One of the easiest clustering algorithms is called K-Means. It’s essentially a way of calculating the distances between data points in an attempt to find the common center within each group of data. In the example above, finding the “common center” would help the streaming service predict how likely you are to prefer one TV show over another.
That machine learning tactic makes sense for advertisers, but what about for cyber security? Clustering can be used in a number of different cyber security techniques, too:
- Analyzing forensics. In the context of cyber security, forensic analysis means taking a very close look at your network traffic statistics in an attempt to detect anomalies.
- Detecting if admin credentials have been stolen. AI can be trained to recognize patterns in remote access that might indicate the presence of a hacker. These could include a normally authorized user logging in at strange times or from a different location than usual.
- Protecting email accounts from malware. Clustering can be useful in preventing your employees from accidentally downloading malware to their computers from a phishing email, as clustering can help to separate legitimate file attachments from more suspicious ones.
Contact SDi for Cyber Security Solutions
Any organization—big or small, public or private—is at risk of a cyber attack. The best way to protect yourself is to bolster your defenses now, not after you’ve been attacked. The financial impact of a single cyber attack is often devastating, and it is far greater than it will cost to invest in your security now. Fortunately, modern uses of applied data science and machine learning for cyber security can help businesses of all sizes gain valuable protection.
Here at Sentient Digital, Inc., we offer technology solutions designed to fit your business. We have staff members well-versed in the latest advancements in artificial intelligence, information technology, cyber vulnerabilities, and much more. When you trust us with your cyber security needs, you know you’ll be in expert hands.
Contact us today to learn more about how we can help your organization stay safe and protect its digital assets.