Machine learning is everywhere around you. It powers the predictive text on your smartphones, makes conversations with chatbots feel natural, and ensures you’re targeted with relevant content on social media. It’s even the brains behind self-driving cars.
It wasn’t that long ago that machine learning (and other areas of artificial intelligence) was considered futuristic. Now it’s become an essential tool for businesses to stay competitive.
In 2020, Deloitte found that 67% of companies are currently using machine learning technology to enhance their business operations — and all but 3% of those surveyed said they’ll be using it the following year.
Every day, we engage with technology powered by machine learning. Here are some real-world examples of it in use:
- Speech recognition by virtual assistants like Alexa and Siri
- Image recognition by social media platforms like Facebook and Instagram
- Automated filters used by banks and other financial institutions to flag fraudulent activities
It even plays an important role in cybersecurity by helping systems identify and respond to potential security risks.
In this article, we’re going to look at machine learning and its benefits. Then, we’re going to see how you can use it to enhance your cybersecurity strategy.
What Are the Benefits of Machine Learning?
Some call it the Age of Automation. Others call it the Fourth Industrial Revolution.
Whatever you decide to name the time we’re living in, one thing is for certain –– it’s a time of tech development and tech disruption. Machine learning (and artificial intelligence as a whole) enables organizations to stay competitive and keep up with the speed of business.
Some of the greatest benefits machine learning offers include:
- Continuous improvement: Machine learning algorithms are constantly learning from the data they’re exposed to.
- Automation: Machine learning makes it easier to automate specific tasks with greater success. These tasks can be many things, including flagging suspicious network traffic and using chatbots to solve customer service issues.
- Versatility: You can use machine learning to boost efficiency and improve your decision-making process, regardless of the industry you work in.
Of course, machine learning also has its challenges –– and one of the most common issues is data acquisition. Machine learning needs to be fed quality data to improve its performance. If you give your learning models incorrect data, they’ll perform poorly.
What Is the Definition of Machine Learning?
To put it simply, machine learning is a form of artificial intelligence that learns through data and algorithms. Those algorithms look for patterns in data, then use those patterns to learn how to make decisions and predictions.
Machine learning applications (also known as learning models) are designed to emulate the human learning experience. They gradually learn which actions to perform in specific scenarios, and they improve with experience and access to more data.
What makes machine learning special is how learning models understand how to respond in different scenarios without human intervention. One could argue they program themselves based on their experiences and the data they’re given.
But to do that, data scientists must first follow these four steps to create a good machine learning system:
- Choose the training data: This is the data the machine learning model uses to inform its decision-making process. This data can be labeled (has special information assigned to it) or unlabeled (raw data without classifications).
- Select an algorithm: Choose the type of algorithm you want to run on your training data set. The algorithm you select will depend on your data. You can read more about the types of machine learning algorithms here.
- Create the machine learning model: Here, you’ll run different data points through the algorithm to train it to become accurate. Accurate algorithms are otherwise known as a a machine learning model.
- Continue to improve the learning model: Once you’ve trained your learning model, it’s time to finely tune it. You can do this by constantly introducing it to new data. This will improve its problem-solving skills and enable it to perform in a broader range of scenarios.
Now that we touched on the definition of machine learning, let’s look at the training process for building a learning model.
Understanding the Types of Machine Learning
Let’s look at the four types of machine learning methods used. Each method uses a different training process and excels in different areas.
The methods are:
1. Reinforcement learning
Think of this as a high-tech version of Pavlov’s dog experiment. The algorithm is given a task and told what to do. When it performs the task correctly, it’s rewarded. So, it learns through trial and error which actions achieve a desired outcome.
Reinforcement learning is commonly used to:
- Train robots to perform specific actions
- Improve autonomous driving
2. Supervised learning
This is where the programmers act as teachers and the algorithm is a student. The programmers give the algorithm data and tell it the correct output. The algorithm works to achieve the correct output –– and with time and a lot of examples, it learns how to achieve the correct answer using the data at hand.
This method is good for machine learning projects that make predictions and classifications. Examples include:
- Analyzing historical financial data then predicting risks and trends
- Assessing shopping and marketing data to better understand customer behavior
- Classifying binary files into categories like spyware, ransomware, legitimate software, etc.
3. Unsupervised learning
This is the machine learning version of a free-for-all. Programmers give the algorithm data without defining the correct output, so there aren’t any right or wrong answers. It’s up to the algorithm to make sense of the data and what to do with it.
Some examples of unsupervised machine learning include:
- Preventing DDoS attacks with clustering algorithms that identify traffic sources that pose a security threat.
- Grouping social media messages based on their emotion and tone.
4. Semi-supervised learning
This subsection strikes a balance between supervised and unsupervised learning. Under this approach, the learning model uses data where only some of the correct outputs are defined. The algorithm has to make sense of the data points it’s given, just like models using the unsupervised learning approach. But it also has defined data to connect different data points together.
Semi-supervised learning is applied in a broad range of scenarios, including:
- Labeling and ranking web pages in search engines.
- Analyzing and labeling image and audio data.
Machine Learning and Cybersecurity
More cybersecurity platforms are using machine learning to help with pattern recognition. These systems deploy artificial intelligence to detect threats and prevent cybersecurity attacks.
Here are some of the ways machine learning is being used to enhance cybersecurity.
Many Intrusion Detection Systems (IDS) use this technology to protect against data breaches and other forms of cyberattacks. They’ll use neural networks and deep learning algorithms to improve accuracy and reduce false positives when analyzing traffic for potential security threats. This makes it easier for security teams to identify and eliminate dangerous network activity.
Spam and social engineering removal
Spam prevention is another area of cybersecurity that commonly uses machine learning algorithms. Here, systems use natural language processing to identify the way humans communicate naturally online. This enables spam prevention tools to recognize unnatural language patterns that are commonly found in spam and phishing attempts, and then block those messages from going to the customer.
Protection from internal security threats
When you think of cybersecurity tools, you usually think about protecting your systems from external threats. But machine learning is also being used to protect organizations from internal security issues.
Some security platforms are using User and Entity Behavior Analytics (UEBA) to identify potential internal security threats. This is a sophisticated technique that uses machine learning to analyze the behavioral patterns of all the users in your system.
After a learning period, UEBA can distinguish between normal and irregular activities. It can notice accounts that have been compromised or have gone rogue just from looking at their behavior.
For example, UEBA could notice if an account accesses company resources at strange hours. It will then block that account from accessing company systems and alert the security team of the incident.
Automating simple security tasks
Automation is arguably one of the biggest advantages of machine learning –– and cybersecurity isn’t any different.
Machine learning can free up time and resources by automating low-level tasks. These are tasks that have lower risks, such as searching for malware, scanning traffic logs, and triaging security. These are all tasks that can be performed by artificial intelligence, leaving your human agents free to focus on high-level security issues.
The Powerful Combination of Machine Learning and Human Expertise
Machine Learning is a powerful tool you can utilize to secure your IT environment. However, nothing can replace the expertise of skilled cybersecurity professionals. Machine learning along with human experts can further strengthen your security with around-the-clock protection.
With Alert Logic, you get the best of both worlds –– you get intelligence driven by machine learning tools alongside the services of cybersecurity experts in globally based Security Operations Centers (SOCs).
Our Managed Detection and Response (MDR) solutions keep your systems protected through 24/7 monitoring and scanning, combining machine learning with advanced analytics to help you detect network anomalies before they turn into bigger problems.
If you do experience a cyberattack, our team of security experts will immediately alert you so you can mitigate the damage.
Schedule a demo today and see why Alert Logic is the best security solution for your cloud, on-premises, and hybrid environments.