Ransomware Detection Using Machine Learning Algorithms

Ransomware Detection Using Machine Learning 

One administrator looks at security screens, which can best be described as “the Matrix” from his perch at the console. He desperately tries to locate any anomalies that could indicate the greatest threat to your data – ransomware.

One administrator can’t detect ransomware attacks using legacy methods in today’s network environment. It is no longer possible to monitor network traffic and manually analyze logs to detect security threats.

Modern ransomware attacks require new techniques and tools. Machine Learning is one of the latest technologies that can be used to defend against ransomware.

Leveraging Ransomware Detection Using Machine Learning

Is there a better way today to detect ransomware threats, given all the ransomware variants out there and the sheer number of attack vectors that exist on-premises as well? Machine Learning (ML) is a method that detects ransomware.

What’s Machine Learning?

Machine Learning may bring to mind scenes from “Terminator”, where machines become dangerously self-aware. Contrary to what you might think, Machine Learning is actually a good thing for detecting ransomware threats.

According to SAS Analytics Software & Solutions, Machine Learning is “…a method of data analysis that automates analytical model building. Machine Learning is an artificial intelligence branch that relies on the assumption that computers can learn from data and identify patterns, making decisions without any human intervention.

Computers can use large amounts of data to make decisions using special ML algorithms. As more data is collected, these ML algorithms adapt themselves.

Many machine learning algorithms are available today to detect and protect your data from ransomware. Many of the current methods are outdated.

Let’s take a look at the first two ransomware detection methods and see the advantages and disadvantages of using these types of detection methods.

Signature Detection

Since the early days of virus protection, signature-based detection was the de facto standard at detecting malware threats. Signature-based protection relies on the fact that a signature must be able to identify the malware in question.

A signature can be thought of as a fingerprint. The malware signature is the unique fingerprint that allows the software to identify the malware. Signatures are not good for unknown malware, but they don’t cover new threats. Zero-day threats are attacks that exploit an unknown security flaw or bug.

Signatures can be easily circumvented since the signature of malware can be copied or altered in any way. This makes it easy for attackers to bypass this method of identifying and stopping known ransomware variants.

In fact, the so-called “Ransomware-as-a-Service” offerings that can be found on the dark web, play upon this entire premise of changing known malware, including ransomware, to target specific organizations.

Abnormal Traffic Detection

Abnormal traffic detection can be considered an improvement in signature-based detection. Anomalous traffic can be detected using many metrics, including network intrusion detection and any other traffic detections that could be considered malicious.

The main problem with the abnormal traffic detection method is the high false-positive rate. This means that legitimate traffic could be mistakenly classified as ransomware or any other malicious traffic. This can lead to the destruction of legitimate applications, and possibly even data corruption if data exchanges are stopped in mid-stream.

As you can see, both ransomware detection techniques – abnormal traffic detection and signature-based detection – are not reliable solutions for detecting ransomware.

To catch ransomware infections that could target your company more effectively, you need to use new detection methods.

File behavior detection

Machine Learning can be used to detect ransomware in file behavior detection.

Machine learning can predict, which is one of the most powerful tools in the fight against ransomware. Machine learning is similar to human learning in some ways. You learn about your friend over time and their habits. You can predict how your friend will react to different situations after a while.

This behavior is very similar to Machine Learning. Normal code execution is legitimate and all applications must exhibit a specific behavior. ML learns over time how legitimate programs behave by taking in large amounts of data points through specialized analysis. This may include interactive debugging and post-mortem code execution analysis.

ML can identify malicious programs and impostors by conducting a thorough and detailed examination of legitimate code execution.

Software solutions that run on top of computers can “learn” normal and abnormal behavior and make intelligent decisions to prompt specific actions. This is Machine Learning.

Normal baselines can represent normal day-to-day activity, from both a user and resource perspective. These can include file access, logins, user and file behavior, resource usage, and any other relevant key indicators of normal activity.

This “learning” process can take several days or weeks, depending on how much data is needed to determine normal activity statistically speaking. A normal baseline has been established. Any anomalies outside the normal baseline can then be identified and examined.

Behavioral Analytics Systems are the best method to detect ransomware

Machine Learning is used for creating behavioral analytics systems that can detect unusual file behavior. These systems are a great way for ransomware to be stopped from spreading through the file system. Solutions that use ML can recognize abnormalities in file behavior, which could include ransomware-related changes.

Ransomware is a file encryption program that holds your data hostage. You are then forced to pay ransom to get your data back. Ransomware not only holds your data hostage but also uses file encryption to do so.

The Maze ransomware uses threats of leaking data as leverage to get the victims to pay the ransom. This elevates ransomware to an entirely new level. Machine Learning ransomware detection will be essential as ransomware threats and capabilities evolve.

Why backup solutions need Ransomware detection built-in

Backups are only part of the solution to the Maze ransomware, which not only holds your data hostage but also threatens you with data leakage. Even if your backups are effective and you can restore your data quickly you could be at risk of your sensitive data being leaked to the dark web.

This highlights the need for a dual approach to protect your files from ransomware and have backups. Even if your initial defenses are not effective, ransomware can still infiltrate files. You want to be able to stop it as soon as possible. This will ensure that criminals who threaten to release data are limited in scope if any.

After the initial ransomware threat is eliminated, backups can be put in place to mitigate the damage that was caused by ransomware. This helps to illustrate why backup solutions need built-in ransomware detection to be effective in the landscape of today’s ransomware threats and capabilities.

Spinone – Unique Ransomware Protection Using Machine Learning

Cloud migrations are on the rise and attackers know that cloud environments can be used to hold your files hostage. There have already been proven means to infect cloud environments either via file synchronization of ransomware-encrypted on-premises files or encrypting email inboxes as in the case with RansomCloud.

Software-as-a-Service offerings like Google G Suite and Microsoft Office 365 are prime targets of ransomware infection since these are the two most popular SaaS offerings that your business may use to host cloud storage and email. It is crucial to protect the environments hosted on these SaaS services.

Spinone is the solution that makes a difference in the cloud backup and security space. Spinone is a unique solution. It provides both enterprise-grade cloud backups and Machine Learning-enabled ransomware protection for abnormal file behavior.
Spinone examines file-level behavior to identify anomalies. Spinone will block the source of ransomware encryption patterns and revoke access to the user account to stop further encryption.

SpinOne’s ML-based logic doesn’t block victimized user’s accounts so they can continue to work. This is an enormous benefit, as it could be the CEO’s account that was hijacked. The account will allow the user to continue working while protecting their resources from any future attacks.

After blocking the ransomware attack, SpinOne’s data protection capabilities kick into action as its unique ransomware Protection module automatically restores any files that were affected by the attack. SpinOne’s restore operations are limited to the affected files. This operation is performed without the need for administrator intervention or manual actions.

Machine learning in Spinone

Administrators are automatically notified about ransomware attacks and Ransomware Protection restore operations in addition to the automated Spinone actions.

Spinone Ransomware Protection Workflow

  1. Identify new ransomware attacks
  2. Block access automatically in real-time
  3. Identify encrypted files
  4. Automatically recover files from the last successful backup
  5. Send Security Alert to alert administrators about a ransomware attack

Spinone is the only company that offers a true end-to-end solution for fighting ransomware in SaaS cloud environments. This ensures 100% accurateransomware protection of your cloud files.

Spinone Backup and Security

Apart from the Ransomware Protection module that Spinone includes, it offers a variety of cybersecurity defenses to stop ransomware before it can infect files in your SaaS environments. Additional security features of Spinone include:

  • Avoid risky apps – Block third-party apps from integrating with your SaaS environment
  • Behavior analysis – Detect abnormal behavior from end-users
  • Insider threat Prevention – Effectively identify malicious end-users or compromised credentials
  • Brute force login detection – Provides visibility for brute-force log attempts to your SaaS environment
  • Abnormal Data Download Protection – Does a user perform an unapproved data download? Data leakage can be identified and prevented
  • Alerting and Reporting – Realtime reports and alerts based on ML intelligence
  • Policy-based Control – Create different policies based upon different business units or other organizational boundaries

Free Fully-Featured Trial Of Spinone

What is the prevalence of machine learning in cybersecurity?

Leading cybersecurity experts are recognizing and publicizing the need for Machine Learning used in the cybersecurity fight. Ransomware attacks are becoming increasingly sophisticated and require that the most powerful computing power be used to stop them.

Attacks have become more sophisticated and large-scale.

  • Advanced techniques and attackers
  • Cybercrime has taken over the internet.
  • Collateral damage is growing

Why is ML so relevant now more than ever?

  • The power of computation has never been greater
  • The volume of data has exploded
  • There are many data sources available
  • Data can be quickly collected and quickly analyzed.
  • Computing costs are now lower than ever
  • Open-source free tools
  • Codesharing within the community

Machine Learning is being used by companies already

It doesn’t take long to find large companies that already use Machine Learning in their security solutions. These are just a few examples.


  • Currently using ML in the Windows Defender Advanced Threat Protection solution that is part of Windows 10. It updates automatically and uses cloud AI and machine-learning algorithms to spot threats.


  • Splunk’s Enterprise Security and Splunk User Behavior Analytics use machine learning to detect threats so they can be identified and eliminated quickly


  • Blackberry currently specializes in cybersecurity solutions that utilize machine learning. They recently purchased Cylance, which uses AI/ML logic to secure cybersecurity.

These three examples represent just a small number of large companies that are using Machine Learning to enhance their cybersecurity products and solutions. Machine Learning is expected to become more common in cybersecurity as companies realize its effectiveness in eliminating security threats.

Conclusion Thoughts

Ransomware variants today require new technology and tools to combat them. The traditional detection and protection tools, which use signature-based detection or abnormal Traffic detection, are not effective against ransomware attacks.

It is possible to quickly detect ransomware activity by using solutions that use leverage to detect unusual file behavior. Spinone is an innovative solution that takes advantage of this approach. Spinone uses Machine Learning to power the abnormal file behavior engine. It also provides backups for your cloud environment.

Using these tools together effectively, Spinone can stop ransomware and revert any damage it may have caused automatically.