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Detecting Ransomware on Networks at Scale Using Traffic Analysis

  • Pallavi Pavithran

Ransomware attacks are still causing serious financial and reputational damage to organizations. In May 2024, they made up 32% of all reported cyber incidents, and 92% of industries saw them as a major threat. 

These attacks lock important data and ask organizations for payment to regain access. Even after paying, attackers might have already gone through the whole system and left loopholes for future attacks. 

In some cases, ransomware hides in a network for a long time before being noticed. That’s why finding it early and taking action is important. Early detection of this malicious software helps secure sensitive data, reduces response time, and prevents organizations from facing financial demands.

How Does Ransomware Spread Through a Network?

Check the process of how attackers execute ransomware attacks:

  • Entry into Systems

    Ransomware usually gains initial access by tricking users into installing harmful software. After that, its ransomware payload places malicious code on the victim’s device.

  • Typical Attack Progression

    The ransomware attack usually follows a series of steps:

    1. Infection: The malware enters and takes hold within the system.
    2. Encryption: It starts locking or encrypting files, so users can't access them.
    3. Communication: The malware contacts its command and control servers to exchange information, coordinate further actions, or send ransom demands.

  • Double Impact of Ransomware

    Besides encrypting files and changing file extensions to hold them hostage, ransomware may also steal sensitive data before encryption. This stolen data can be used for additional leverage against victims, increasing the threat beyond just file inaccessibility.

Worried About Ransomware? Protect Your Data with Smart Solutions

Key Highlights:

  • Understand how attackers lock your data hostage
  • Use powerful detection, deception, and response tools
  • Gain full visibility across endpoints, networks, and cloud
  • Equip your security team with actionable threat intelligence
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The Role of Network Traffic Analysis in Ransomware Detection

A malware attack can cause unusual changes in normal network traffic and activities: 

  • Network Activity Signals Ransomware:
    Ransomware usually creates unusual outbound traffic when communicating with outside control servers. Watching this activity helps detect malware on the network.
  • Separating Malicious from Normal Traffic:
    One of the challenges is telling apart ransomware’s network behavior from everyday legitimate data flows. Analyzing patterns and behaviors within network packets helps identify suspicious communication that indicates ransomware.
  • Detecting Early Alerts through Network and Packet Analysis:
    Network security solutions constantly monitor traffic to catch early signs of ransomware and data breaches before the data is locked. This helps enable faster alerts and fix issues.

What are the Challenges and Considerations of Ransomware Detection Using Network Traffic Analysis?

  • Limitations of Signature-Based Detection:
    Traditional methods that look for known malware often miss new or specific ransomware variants because they change their code to hide. This makes purely signature-based detection approaches less effective as attackers constantly develop fresh variants.
  • Need for Behavior and Traffic Analysis:
    Since ransomware changes often, it’s important to watch for unusual network activity instead of just looking for known signs. Spotting strange traffic can help find ransomware even if it’s new.
  • Balancing Speed and Precision:
    Early detection is essential to prevent ransomware attacks, but the solution you choose must avoid excess false alarms. This can overwhelm security teams and delay addressing real ransomware attacks.

Machine Learning for Network-Based Detection

Using machine learning in network traffic analysis helps organizations detect ransomware more effectively. 

Analyzing Network Traffic with Machine Learning:

Machine learning looks at network traffic patterns to find suspicious behavior that could signal ransomware. 

Key benefits of using machine learning include: 

  • High Accuracy: These models can accurately distinguish between normal and malicious traffic.
  • Detecting New Threats: Unlike old methods that look for known signs, machine learning spots new ransomware by learning how it behaves.

Practical Impact:

Implementations of machine learning and other security tools in ransomware detection have shown strong results, enabling early and precise identification of ransomware based on network behavior before significant damage occurs.

Core Techniques in Advanced Network Traffic Analysis for Ransomware Detection and Recovery

Below are the main techniques in advanced network traffic analysis that can be effectively utilized for advanced ransomware detection and recovery.

  • Real-Time Network Monitoring:
    Watching network traffic in real-time helps security systems spot unusual activity right away. This quick detection makes it easier to catch signs of ransomware before it causes serious harm.
  • Deep Session Inspection:
    This method looks closely at all network data, even encrypted traffic when it can. By analyzing what’s exchanged, it can find hidden signs of data theft or malware that basic checks might miss.
  • Advanced Traffic Analysis:
    Beyond surface-level checks, sophisticated traffic analysis solutions focus on patterns such as unusual volume spikes or communication with suspicious external servers. This helps differentiate between normal and malicious network traffic.
  • Automated Risk-Aware Network Mapping:
    Security platforms automatically create detailed maps of the network infrastructure, showing how devices are connected and what data is flowing where. This “terrain mapping” identifies critical assets and detects abnormal lateral movement — when malware spreads from one device to another inside the network.
  • Identification of Vulnerabilities and Suspicious Behavior:
    By constantly checking network traffic and connections, these methods can find weak spots that attackers might use and spot suspicious activities like unauthorized data transfers or unusual device communication.

Integrating Network Detection with Endpoint and Deception Technologies

Integrating network detection with endpoint detection and deception technology can ensure your network is secured from ransomware, along with connected systems and devices.

  • Holistic Security Through Integration:
    Combining data from network monitoring with endpoint detection solutions provides a full picture of an attack. While network detection identifies suspicious traffic, endpoint tools focus on unusual behavior on individual devices, creating comprehensive ransomware protection.
  • Deception Technologies as an Early Warning:
    Using decoys, traps, or fake assets within the network can mislead attackers. When attackers interact with these fake resources, security teams get alerted early, gaining valuable time to respond and understand attacker methods.
  • Automation for Fast Threat Response:
    Automated solutions can isolate infected devices quickly and block harmful traffic when unusual patterns or activities occur. These immediate actions stop ransomware infection from spreading further and protect the network.

Benefits of Coordinated Defense:

  • Enhanced detection accuracy by correlating endpoint and network signals
  • Faster containment of threats through automated quarantine
  • Improved attacker tracking by analyzing deception interactions alongside network and endpoint data

Benefits of Early Network-Based Ransomware Detection

  • Reducing Financial Impact and Downtime:
    Finding ransomware early helps avoid paying ransoms, replacing systems, and long downtime. Quick identification allows organizations to act before ransomware can encrypt data or cause data loss, saving significant resources.
  • Safeguarding Sensitive Information and Reputation:
    Early threat warnings prevent unauthorized access or data theft. Protecting data from all breaches enhances customer trust and organizational reputation.
  • Accelerating Incident Response and Recovery:
    Quick detection lets security teams separate infected devices and stop the ransomware from spreading. This helps clean systems faster, restore data from backups, and reduce damage to the network.

How Does Fidelis Network® Help in Ransomware Detection and Recovery?

To fight ransomware effectively, organizations need clear network visibility and fast threat detection. Fidelis Network® helps by providing: 

  • Full visibility across all network ports and protocols, enabling comprehensive monitoring of data in motion. 
  • Advanced security tools like deep session inspection find hidden data theft and activity from ransomware and other malware. 
  • Automated risk-aware network mapping that uncovers suspicious lateral movement and vulnerable assets. 
  • Rapid automated response features to quickly isolate threats and prevent their spread. 
  • Proven to help customers detect attacks after a breach up to nine times faster. 

By combining these features, Fidelis Network® gives organizations the solution and confidence to detect ransomware early, reduce damage, and keep business running despite changing cyber threats.

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Frequently Ask Questions

What is ransomware and why is it dangerous?

Ransomware locks your important data and demands money to unlock it. Even after paying, attackers may have already caused damage and left your system vulnerable. 

How can network traffic analysis help detect ransomware?

Network traffic analysis helps detect ransomware by: 

  • Identifying unusual patterns 
  • Detecting suspicious communications 
  • Spotting early signs of malicious activity before significant damage occurs 

How does machine learning help detect ransomware?

Machine learning spots suspicious network behavior and can catch new ransomware that traditional methods miss. 

How does Fidelis Network® protect against ransomware?

It provides full network visibility, detects hidden threats, maps risk across the network, and rapidly isolates infected devices to stop ransomware threats before they spread.

About Author

Picture of Pallavi Pavithran
Pallavi Pavithran

Pallavi is a tech writer with a deep enthusiasm for cybersecurity and emerging technologies. With a keen interest in digital security, she simplifies complex concepts and provides valuable insights to help businesses stay ahead and effectively navigate the ever-evolving cybersecurity landscape.

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