Ransomware Detection Using Machine Learning

Gone are the days of manual security analysis that cyber security teams used to perform to track down and stop ransomware attacks.

Due to the vast amount of data that is being generated every minute of every day, it has become impossible to rely on a few cyber security professionals to stop ransomware attacks without significant assistance from automated security tools.

As additional devices generate more and more data, it is unfeasible to expect to have any reliable defenses for ransomware without employing security tools with advanced detection and prevention capabilities.

Through research and innovation, one of the most reliable methods for stopping ransomware attacks has become Machine Learning. The technique of data analysis using analytical models combined with automation has yielded what is now known as Machine Learning.

It is not possible to ingest an enormous amount of data and perform analysis using various machine learning algorithms. The key to an effective analysis is large amounts of data ingested over a larger period for the most accurate results. Other methods of detecting and preventing ransomware are still in use, however, they are out of date in terms of effectiveness. Nonetheless, it is worth mentioning different approaches to emphasize the key values of using machine learning to stop ransomware attacks.

Stopping Ransomware – Approaches that are in the Past

Cyber security teams relied on various technologies to assist them in the incident response process. As technology improved and capabilities increased, many approaches from the past are not considered to be obsolete in terms of stopping more advanced forms of cyber security attacks.

Signature-based Detection

The oldest method of detecting malware was antivirus solutions that relied on malware signatures to detect different forms of malware. As each human has a different fingerprint, all files are uniquely recognized by their computed hash values, a string of characters of a fixed length that map any data size file. The signature-based method worked well with known malware strains but fails miserably when tasked to detect unknown threats since there is simply no capability within the technology to do that. Due to this inherent flaw, additional detection methods were developed that relied on detecting anomalies within the environment.

Anomaly-based Detection

Considered to be a step up from signature-based detection, traffic anomaly detection mechanisms have given cyber security teams a fighting chance at the time of adoption. Since malware presence almost always causes anomalies in network traffic and in file behavior on a system, deploying this technology has allowed for more insight into anomalies that could indicate a ransomware attack.

Many organizations still rely on traffic anomaly detection due to the lack of a serious cyber security budget, and even though this approach is better than using signature-based technology, anomaly-based detection alone does have its downsides.

One of the more serious “side-effects” of network anomaly-based detection is the amount of data that is being generated for analysis. Due to the large volume of traffic, there is an increased number of alerts being generated by security solutions. Consequently, many alerts turn out to be false positives. This has effectively burdened cyber security teams with more and more alerts and not enough time to analyze all of them, leading to alarm fatigue.

Enter the Machine Learning

Away from signature-based detection and traffic anomaly analysis techniques comes file behavior detection. Implementing machine learning with file behavior analysis produces a powerful tool for thwarting ransomware attacks. When a security solution is deployed, the next phase in preparing the tool for an effective analysis is the learning phase. The solution observes and analyzes file behavior and all related processes, command-line execution, network traffic generation, and more. Based on the learned behavior, the security solution recognizes what the usual behavior is for every system.

Machine learning provides a unique capability of detecting any shifts in the way files behave, effectively triggering an alert when an anomaly occurs. Over time, this significantly improves the cyber security team’s effectiveness since there is no overwhelming number of alerts, but only meaningful detections with insight into all file-related process executions and network requests.

By thoroughly examining legitimate code executions, machine learning systems can create baselines and detect any behavior that deviates from those baselines. With many ransomware strains being created, machine learning provides the best form of protection against sophisticated ransomware attacks by alerting the cyber security teams of any anomalies that the attack causes. This includes file behavior anomalies which are most common in ransomware attacks.

Latest ransomware strains perform data exfiltration along with file encryption, providing a whole new concern for organizations. It is becoming increasingly obvious that without machine learning algorithms, organizations will not be effective in stopping ransomware attacks. The days of manual log analysis and inspection are over and with an increase of data being generated, it is becoming more difficult to perform effective security analysis without the assistance of machine learning solutions. Signature-based detection has fallen out of the category of effective incident response as ransomware strains change and new variants are being rapidly introduced.

Machine learning is thus the lone wolf in the fight against ransomware and will be a key arsenal for many organizations seeking to protect themselves from debilitating sensitive data exfiltration and file encryption malware. By learning the usual behavior and creating baselines, machine learning reduces the number of false positive alerts and provides the exact insight needed for ransomware detection and prevention. File behavior anomalies exhibiting unusual network traffic, process execution or any other action will be detected by a properly deployed machine learning solution.