Ransomware is a type of malware that encrypts files on a device and demands payment for decryption. Detecting ransomware quickly is crucial to limit damage and avoid paying ransoms. There are several ways that ransomware can be detected before encrypting files or while active on a system.
Signature-based detection
Many anti-virus and anti-malware tools rely on signature-based detection to identify known ransomware. Security companies analyze ransomware code to extract unique signatures that can be used to detect the malware. Signature files are periodically updated to address new threats. This method is fast and efficient at detecting known ransomware, but fails to catch new variants.
Behavioral analysis
Behavioral analysis looks at the actions of programs to determine if they resemble known malicious behavior. For example, an unknown program that starts encrypting files with an uncommon encryption algorithm may be flagged as ransomware. Other behaviors like modifying boot settings, deleting volume shadow copies, and generating ransom notes, can also trigger alerts. Behavioral analysis can detect new ransomware with similar attributes to existing threats.
Deception technology
Deception tech sets up fake directories, files, and servers that appear like legitimate resources. Once accessed, alerts are triggered that the “deception” resources have been touched. Ransomware scanning systems to encrypt files will often get caught in these traps. Admins are notified of the incident without real resources being impacted. Deception tech can detect and isolate ransomware before damage is done.
File integrity monitoring
File integrity monitoring (FIM) tools record and track changes to critical files and directories. Common targets of ransomware like document files, databases, and backups are monitored. Any unauthorized modifications to these areas like encryption or deletion are flagged and alerts generated. FIM provides early warning of ransomware encrypting data before extensive damage is done.
Honeypots
Honeypots are systems designed to be probed, attacked, and potentially compromised. They act as decoys that distract adversaries away from production systems. Ransomware that infiltrates networks may stumble upon honeypots and start encrypting dummy files. This triggers alerts of malicious activity without any real assets being impacted. Honeypots serve as an early warning system against ransomware infections.
User behavior analytics
User behavior analytics (UBA) monitors patterns of human behavior. Significant deviations from normal activities can indicate credential compromise or system misuse. For example, a user encrypting terabytes of data that they would never normally touch might suggest ransomware activity. UBA solutions detect these abnormal behaviors and can cutoff system access to contain the threat.
Memory analysis
Analyzing a program’s memory utilization can reveal attributes common in ransomware. Signs like reflective DLL injection, entropy changes before and after encryption, and large numbers of cryptographic function calls are telling. Memory forensics can detect ransomware based on these anomalous characteristics before files are encrypted.
Network traffic analysis
By monitoring network traffic, security teams can spot signs of ransomware communication in the early stages. Unusual internal connections between endpoints, data exfiltration, and traffic to command and control servers, may indicate malicious activity. Deep packet inspection can look for artifacts like new encryption keys being distributed that are hallmarks of ransomware campaigns.
Containment technology
Containment tech isolates suspicious endpoints into virtual containers to safely analyze their behavior. Ransomware that is detonated has its activities restricted to the container. This allows its full capabilities to be understood without compromising the rest of the network. Containment enables ransomware to be detected and studied in a completely isolated environment.
Cloud workloads
For cloud workloads, capabilities like cloud access security brokers (CASBs) can be used to detect ransomware. CASBs employ data loss prevention (DLP) tools to spot anomalous data movements like bulk encryption. They can also block suspicious connections to command and control servers.
Mobile devices
On mobile devices, ransomware can be detected through analyzing apps, monitoring network connections, using behavioral analysis, and more. Anti-virus software on mobile devices detects known ransomware via signatures. Monitoring network traffic can also identify command and control communications.
User training
One of the most effective ransomware defenses is training users to identify social engineering techniques, suspicious emails, and understand security best practices. Human-detection of ransomware spear phishing emails and scam attachments stops infections before they happen. IT teams can enable users through education.
Consequences of ransomware detection
When ransomware is detected, swift action is required to limit damage. Users are alerted, connections are cutoff to isolate the threat, and a full investigation ensues to determine scope. Systems may be taken offline or segments of the network temporarily shut down. Backups will be verified as intact and uncompromised. With early detection, organizations can contain the incident.
Challenges to ransomware detection
Ransomware creators employ various tactics to evade detection:
- New variants avoid traditional signatures
- Slow encryption stays under the radar
- Targeting unused files or depths of directories
- Mimicking legitimate software like backup tools
- Disabling security tools
- Lying dormant before detonating
These evasion methods highlight why a defense-in-depth approach is required to reliably detect ransomware across an environment.
How to test ransomware detection
Testing ransomware detection tools is an important exercise for organizations to validate their preparedness. Some methods include:
- Simulated attacks by red teams
- Using ransomware samples in isolated test environments
- Introducing decoy resources and trying to encrypt them
- Monitoring tool efficacy during simulated incidents
- Assessing detection capabilities across multiple platforms
Testing should cover the various stages of attack to ensure detection works. Real-world ransomware malware and attack methods should be leveraged to strengthen defenses.
Ransomware detection in the cloud
Protecting against ransomware in the cloud introduces challenges due to the dynamic environments and shared responsibility models involved. Cloud-native detection methods include:
- Cloud access security broker (CASB) solutions
- File integrity monitoring of cloud storage
- Monitoring traffic between cloud segments
- ML anomaly detection APIs offered by providers
- Third-party cloud security posture management
- Native cloud logging and alerting tools
A combination of cloud-specific detection tools alongside traditional controls provides layered security against ransomware campaigns targeting cloud environments and assets.
How machine learning improves detection
Machine learning (ML) techniques significantly enhance ransomware detection capabilities by:
- Detecting new ransomware with no previous signatures
- Identifying anomalies and outliers that may be ransomware
- Correlating across multiple data sources to improve accuracy
- Adaptively learning normal behavior patterns
- Automating analysis of enormous volumes of network data
- Empowering predictive ransomware threat models
ML systems trained on studying normal behaviors can automatically flag activities that deviate from baseline models as potential ransomware. Their analytical capabilities augment human detection.
Paying the ransom
If ransomware successfully encrypts files, organizations face a difficult decision on whether to pay the ransom. There are several factors to consider:
- Paying encourages and funds more attacks
- There is no guarantee files will be recovered
- Cyber insurance may cover the ransom payment
- If the data is mission critical, paying may be required
- Decryptors may be available from police without paying
- Bans on paying ransoms exist in some geographies
The ultimate decision depends on the specific circumstances of the attack. But paying the ransom should only be a last resort option.
Reporting ransomware attacks
If impacted by ransomware, organizations should report the incident to law enforcement agencies like the FBI or Secret Service. Reporting helps authorities:
- Track ransomware groups
- Understand the threat landscape
- Disrupt attack infrastructure
- Aid future victims
- Build intelligence on attackers
- Issue alerts on ongoing campaigns
Organizations can usually file complaints regardless of if the ransom is paid or not. Reporting allows collective information sharing to strengthen overall ransomware resilience.
User education
User education is key to empowering human detection against phishing emails and social engineering tactics that deliver ransomware. Training should cover topics like:
- Identifying suspicious links and attachments
- Avoiding browsing to disreputable sites
- Spotting fraudulent login pages
- Reporting suspected phishing attempts
- Handling sensitive data properly
- Following least privilege and separation of duties
Equipping users to recognize threats and exercise caution significantly improves resilience against ransomware before it ever impacts devices and networks.
Ransomware defense strategy
A multi-layered strategy is required to defend against the ransomware threat effectively:
- User education and training
- Email security and spam filtering
- Web filtering and DNS blacklisting
- Vulnerability patching
- Privileged access management
- File integrity monitoring
- Backup verification testing
- Network segmentation
- Behavioral analysis
- ML anomaly detection
With overlapping defensive technologies and user awareness, organizations can detect ransomware early and mitigate damage from attacks.
Ransomware response plan
Incident response plans should outline specific procedures for dealing with ransomware events. The plan may include steps like:
- Isolate infected systems immediately
- Shut down access to shared drives and servers
- Verify integrity of backups
- Determine the variant and scope of encryption
- Check for lateral movement in the network
- Communicate with employees and customers
- Report to management on next steps
- Retain malware samples and logs for forensics
- Report the incident to authorities
With an actionable response plan, reaction to detected ransomware can be swift, coordinated, and effective.
Conclusion
Ransomware represents a severe cyberthreat facing organizations today. Through technologies like signature scanning, behavior monitoring, deception, and ML, ransomware activity can be identified early before extensive damage is inflicted. But no single method will catch every threat, making layered detection essential. Organizations should test detection regularly and educate users on warning signs as part of a mature ransomware defense posture.