You are a security analyst. It is 3 AM. Your SIEM lights up with an alert. A user is downloading large amounts of data from a file server. You investigate. By the time you confirm it is ransomware, the encryption is already 50% complete.

This happens every day. Ransomware attacks are fast. Human responders are slow. By the time you detect the attack, the damage is done.

But what if you could predict the attack before the encryption starts? What if you could detect the behaviors that precede ransomware, not the encryption itself?

This is the promise of machine learning in ransomware defense. 

It is not about detecting the ransomware. It is about detecting the attack chain that leads to it.

Let me show you how it works, why it matters, and how to implement it.

The Ransomware Attack Chain

Ransomware does not just appear. It follows a predictable sequence.

Stage 1: Initial Access

There is access to the target. It can be done through phishing emails, credential harvesting, and vulnerabilities. At this point, the attacker is in the network.

Phase 2: Reconnaissance

There is reconnaissance of the network. The attacker is looking for file shares, backup servers, and information. He is getting to know the network.

Stage 3: Lateral Movement

Lateral movement through the network is done by the attacker. He seeks more entry points looking for the best targets to exploit.

Stage 4: Privilege Escalation

The attacker gets admin rights. This means that he is able to turn off all the security controls and log files. The attacker controls the entire network.

Stage 5: Preparation

The attacker prepares for the final stage.They deploy the ransomware. They delete backups. They disable antivirus.

Stage 6: Encryption

The ransomware encrypts your files. The attack is now visible. But it is also too late.

Machine learning can detect the first five stages. It can predict the attack before the encryption starts.

What Machine Learning Can Detect

Machine learning models are trained on billions of events. They learn what normal behavior looks like. They detect anomalies which reveal that there is an ongoing attack.

File Access Anomalies.

When a user who usually accesses 10 files per day begins to access 10,000 files within an hour, that's a warning sign that tells you something is wrong. The attacker is preparing the data for encryption.

Process Anomalies.

A process that doesn't write to any encrypted files suddenly begins doing that.This could be a sign of ransomware preparation.

Anomalous network traffic.

There is an unexpected communication with the internal server, which suddenly becomes large in volume and is directed to an external IP. This might be a case of data leakage.

Behavioral anomalies of a user.

A user who was logging in during working hours, all of a sudden starts doing so at 3 in the morning. This might indicate stolen credentials.

Anomalies of users' administration.

If a user rarely performs tasks involving administrative privileges and starts making admin accounts or turning off security features, then this points to the presence of an ongoing attack.

Anomalous process ancestry.

The creation of a process by a strange parent process. A word processor should never spawn a PowerShell process, for example. This is typical of malicious activity.

How Machine Learning Models Are Trained

Machine learning models are trained on historical data. They learn what normal behavior looks like. They learn what attack behavior looks like.

Supervised learning.

Training of the model uses labeled data. This consists of examples of good behavior and bad (attack) behavior. The model learns how to tell these apart from each other.

Unsupervised training.

This involves use of unlabelled data, and the model learns about normal behavior, and all non-normal behavior is detected by the model.

Reinforcement Learning.

Through making errors, the model is able to learn and is then rewarded for right predictions while being punished for wrong ones.

Ensemble Learning.

Several models work together to increase accuracy because each one identifies patterns differently. The combination proves more robust than individual models.

Real-World Detection Examples

Example 1: Suspicious Accessing of Files

An example of machine learning detection could be the case where a machine learning system detects a user who is accessing 5,000 files in 10 minutes. On a daily basis, this user is known to access just 20 files. As such, the system raises an alarm about the suspicious activity. 

Investigation reveals the theft of the user’s credentials. The attacker was setting up files to be encrypted.The analyst revokes the credentials and stops the attack before encryption starts.

Example 2: Suspicious Chain of Processes

The machine learning algorithm identifies that the Microsoft Word application has launched a PowerShell process. This kind of activity is unusual in relation to Microsoft Word application. The algorithm detects this unusual activity. 

The analyst conducts some investigation and finds out that the Word document contained malware in the form of macros trying to download ransomware. The analyst stops the attack from happening by stopping the downloading of the file.

Example 3: Uncommon Administrative Activity

The machine learning algorithm identifies that the user has created a new admin account in the middle of the night. The user did not create an admin account before. The algorithm reports this suspicious activity. 

An analyst conducts investigation and finds out that the user’s account was hacked and the hacker was about to launch ransomware. The analyst disables the account and prevents the attack.

The Role of Behavioral Analytics

Behavioral analytics plays an important part in ransomware detection using AI. Behavioral analytics studies actions of users/systems, rather than attributes.

User Behavior Analytics.

UBA analyses user activity on the system. UBA creates a baseline of regular activity. Any deviation from the baseline is detected.

Entity Behavior Analytics.

EBA analyses activities of systems in interactions with each other. EBA creates a baseline of regular activity. Any deviation from the baseline is detected.

Network Behavior Analytics.

NBA analyses network traffic. NBA creates a baseline of regular network activity. Any deviation from the baseline is detected.

Challenges and Limitations

False positives.

Machine learning models can produce false positives. These may be where legitimate activities are considered suspicious. This can burden analysts and cause alert fatigue.

False negatives.

Machine learning models can generate false negatives. They may miss an attack. This can lead to a breach.

Adversarial attacks.

Attackers can evade machine learning models. They can modify their behavior to avoid detection. They can poison the training data.

Data quality.

Machine learning models require high-quality data. They need clean, labeled data. They need representative data.

Model drift.

Machine learning models degrade over time. They need to be retrained regularly. They need to adapt to new threats.

Deployment of Ransomware Detection Using AI

Step 1: Collection of proper data.

In order to train a machine learning algorithm, you need proper data. Collect data from endpoints, network, and users' behavior. Collection of data can be in form of processes, files, network traffic, and users’ activities.

Step 2: Selection of proper model.

Choose a model which will fit your needs. You can opt for supervised machine learning in case of known threats while for unknown threats you can opt for unsupervised machine learning.

Step 3: Training and validation of the model.

You can train your machine learning model with help of past data and also validate it with different datasets.

Step 4: Model Deployment.

Deploy the model in your environment. Make sure that the model will be compatible with your SIEM, EDR, and any other tools used by you.

Step 5: Monitoring and Improvement.

Monitor the efficiency of the model. Note all the false positives and false negatives. Retrain the model on a regular basis. Adjust for new threats.

The Bottom Line

Machine learning can predict ransomware attacks before encryption starts. It detects the attack chain that leads to ransomware, not just the encryption itself.

It detects anomalous file access patterns, suspicious process chains, unusual administrative activity, and other indicators of compromise.

It is not perfect. It generates false positives and false negatives. Attackers can evade it. But it is a powerful tool that can stop ransomware before it encrypts your data.

Combine machine learning with other defenses. Use it to supplement, not replace, your existing security controls. Train your analysts to respond to alerts. Keep your models updated.

The ransomware is coming. Make sure you see it before it encrypts.

FAQ Section

Can machine learning predict ransomware before any encryption takes place?

Yes. Machine learning models can detect the steps which lead to ransomware attacks, such as reconnaissance, lateral movement, and privilege escalation. Thus, by detecting the attack chain, AI can predict ransomware attacks prior to the encryption.

What type of information does AI use to identify ransomware?

AI machine learning models examine access, process execution, network usage, user behavior, and administration actions. A baseline of normal activity is established, and deviation from this baseline is used as an indication of ransomware attacks.

What is the accuracy of ransomware detection by means of AI?

The accuracy of such detection largely depends on the model used and on its context. High levels of accuracy might be achieved through ensemble models and behavioral approach. Yet, any model is not perfect and can be hacked.

Are there ways that attackers can get past AI-based ransomware detection systems?

Yes. The attacker can change his behavior, can employ LOLS or poisoning, hence evading the system. This is one of the reasons why using AI on its own cannot be considered secure.

What is the process for detecting AI ransomware?

In order to detect AI ransomware, there are several steps involved; the first being gathering the data required by the model at the endpoints, in the network, and from the user. Select a model and train and test it.