How machine learning algorithms detect ransomware attacks

12 April 2023

By Zac Amos, Features Editor,

How can businesses and users stay ahead of the ever-evolving risk of ransomware attacks? An increasing number of cyber attacks occur every year, posing a serious threat to users’ privacy and financial well-being. Fortunately, machine learning can give users an edge over hackers using pattern recognition and behavioral analysis.

Leveraging pattern recognition

The estimated global cost of cyber crime rose over 900% between 2018 and 2022. Ransomware attacks are a particularly lucrative type of cyber crime contributing to that trend. The rise of ransomware-as-a-service (RaaS) is enabling more hackers to launch ransomware attacks with minimal experience and preparation than ever before. As a result, businesses and users need more advanced tools to defend themselves.

Artificial intelligence is emerging as a prime solution to the surging risk of ransomware. AI excels at pattern recognition, which makes it ideal for detecting suspicious activity. There are usually common indicators that a ransomware attack is coming before the encryption malware is actually deployed. Machine learning algorithms can be trained to recognize these indicators and intervene, stopping ransomware attacks.

AI has already been in use in cyber security for years now. In fact, as of 2021, an estimated 56% of businesses are using AI for at least one function. One example of AI in a basic security function is email filters that use pattern recognition to spot spam and phishing content. Most major email providers, such as Google’s Gmail, have some type of phishing email identification system.

Google’s filter uses AI to analyze incoming emails for red flags, such as unusual email addresses, malicious links or suspicious attachments. Phishing attacks are often used to steal credentials for use in ransomware attacks, so tools like this are important. However, AI can go well beyond email analysis.

Adapting to evolving attacks

Part of the challenge of fighting ransomware attacks today is the speed at which these attacks change. Hackers are constantly working on new attack strategies and developing new ways to get around security protocols. Businesses and users need network defense tools that can adapt to ever-changing threats.

Traditionally, machine learning requires large amounts of data to learn from. However, this is becoming an issue today because many developers don’t have much data on emerging ransomware threats. Luckily, advanced types of machine learning can be trained without a wealth of training data.

Using unsupervised learning or self-learning training models, machine learning algorithms can essentially learn on the job. These models adapt to learn the typical user behaviors of a specific network environment and base threat detection on those unique patterns. So, instead of training the AI to spot unusual behavior, developers allow it to learn normal behavior and distinguish anything that is not within those normal patterns.

Hackers don’t typically behave like an authorized user. They may access unusual types of files or run new types of programs. The machine learning algorithm might not understand why a user is behaving this way, only that it does not fit into expected behavior patterns. That alone is enough to prompt the algorithm to flag the suspicious activity as a potential security threat.

Hackers using new types of ransomware attacks are often betting on the probability that their victim’s security programs won’t be able to recognize a new threat. With this adaptive machine learning strategy, though, the algorithm doesn’t have to be familiar with a particular ransomware tactic to recognize that it is suspicious. In fact, the more new types of suspicious behavior it sees, the better it will get at recognizing potential threats.

Fighting ransomware with machine learning

The threat of ransomware grows every year as attack methods change and evolve. Businesses and users alike need advanced tools to defend their devices and data. Machine learning is the perfect tool for staying ahead of hackers. It can adapt to emerging ransomware strategies and spot suspicious behavior, helping to stop cyber attacks before they happen.

For more from’s Features Editor, Zac Amos, click here. Lastly, to receive cutting-edge cyber security news, exclusive interviews, expert analyses and security resources, please sign up for the newsletter.

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