Analysis SIEM

SIEM Dashboards:

Imagine a SIEM as a security control center. Dashboards within this center provide a visual overview of your security status in real-time. Here’s what they do:

• Triage Alerts: Like a busy emergency room, SIEM dashboards help you quickly assess security alerts. They highlight critical issues (true positives) that need immediate attention and filter out unimportant ones (false positives).

• Monitor Data Sources: Dashboards ensure all your security sensors (like firewalls, intrusion detection systems) are sending data correctly. It’s like checking if all your security cameras are working properly.

• Track Global Threats: Dashboards display information about cyber threats happening worldwide. This helps you understand the current threat landscape and prioritize your own security efforts.

• Vulnerability Scanning and Management: SIEM dashboards can display key information related to vulnerability management, such as:

  • Number of vulnerabilities: Track the total number of vulnerabilities discovered across the organization.

  • Vulnerability severity: Categorize vulnerabilities by severity level (e.g., critical, high, medium, low) to prioritize remediation efforts.

  • Remediation progress: Monitor the number of vulnerabilities that have been addressed and track progress towards remediation goals.

  • Vulnerability trends: Identify emerging vulnerabilities and track the overall vulnerability landscape within the organization.

• Threat Hunting: SIEM dashboards can help identify opportunities for threat hunting by:

  • Visualizing alert trends: Identify unusual spikes in alerts, which could indicate a potential attack in progress.

  • Correlating alerts: Analyze alerts across different sources to identify potential attack chains or coordinated attacks.

  • Integrating threat intelligence: Display threat intelligence feeds to highlight emerging threats and identify potential attack vectors.

  • Analyzing user behavior: Identify anomalies in user behavior that could indicate malicious activity.

Dashboard Visualizations:

• Pie charts:

  • Show the distribution of vulnerabilities by severity level.

  • Display the proportion of alerts categorized as true positives, false positives, or under investigation.

• Line graphs:

  • Track the number of vulnerabilities over time.

  • Monitor the number of security incidents over time.

  • Analyze trends in user login activity.

• Bar graphs:

  • Compare the number of vulnerabilities across different systems or departments.

  • Show the distribution of alerts by source or type.

• Stacked bar graphs:

  • Compare the number of vulnerabilities by severity level across different time periods.

  • Analyze the distribution of security incidents by type across different departments.

• Gauges:

  • Display the overall security posture of the organization, such as the percentage of vulnerabilities remediated or the number of open security incidents.

Analysis and Detection Methods:

SIEMs analyze security data to find threats. They use rules to identify suspicious activity and trigger alerts.

• Simple Rules: These rules are like basic instructions, like “If someone tries to log in from a strange location AND fails, alert me.”

• Challenges: These simple rules often create many false alarms (like a harmless login attempt from a different location). They also struggle to detect new types of attacks that haven’t been seen before (called “zero-day” attacks).

Essentially, SIEMs try to find patterns that indicate a potential threat. However, creating the right rules and filtering out false alarms is crucial for effective threat detection.

Simple rules in SIEMs, like “If X happens AND Y happens, then alert,” are a good start, but they can miss a lot.

Heuristic Analysis is like having a detective look at the bigger picture. Instead of just following strict rules, it tries to understand the “why” behind the events. For example, if someone usually logs in from their office and suddenly logs in from a different country, heuristic analysis would consider this suspicious even if there’s no specific rule against it.

Machine Learning takes this even further. It allows the SIEM to learn from past events, like a detective studying past crime cases. It can identify patterns and anomalies in data that humans might miss. This helps the SIEM detect new and evolving threats that simple rules wouldn’t catch.

• Simple Rules: Like following a strict recipe.

• Heuristic Analysis: Like a chef improvising based on experience and intuition.

• Machine Learning: Like a robot chef that learns and adapts its cooking based on feedback.

By using these advanced techniques, SIEMs can become much more effective at detecting threats and keeping your systems secure.

Behavioral Analysis

Imagine you have a friend who usually arrives at work around 9 AM and leaves around 5 PM. Suddenly, they start arriving at 3 AM and leaving at 10 PM. This unusual behavior might indicate something is wrong.

Behavioral analysis in cybersecurity works similarly. It focuses on understanding the typical behavior patterns of users and devices within a network.

• Building a Profile: The system observes normal activity, like login times, access locations, and file access patterns. It then creates a “profile” of what’s considered normal for each user and device.

• Detecting Anomalies: Any significant deviation from this normal behavior is flagged as an anomaly and triggers alerts. This could include:

  • Unusual login times: Logging in at odd hours.

  • Accessing sensitive data outside of normal working hours.

  • Sudden increase in data transfer activity.

  • Logging in from an unusual location.

By analyzing normal behavior and identifying deviations, behavioral analysis helps detect threats that might otherwise go unnoticed. It’s like noticing when your friend starts acting strangely, which might indicate something is wrong.

behavioral analysis requires time to build accurate baselines. Initially, there might be some false alarms as the system learns and adapts.

Anomaly Analysis: Finding the Unusual

Imagine you’re at a concert. Most people are standing and enjoying the music. Suddenly, someone starts running around wildly. This unusual behavior stands out because it doesn’t conform to the expected behavior of concert-goers.

Anomaly analysis in cybersecurity works similarly.

• Defining Normal: It establishes rules or expectations for how systems and networks should behave. For example:

  • Network Traffic: How data packets should be structured and exchanged.

  • System Processes: How applications and services should run.

• Identifying Deviations: The system constantly monitors for any events that deviate from these established norms. For example:

  • Unexpected network traffic: Packets that violate network protocols.

  • Unusual system activity: Unexpected changes to system configurations or resource usage.

Benefits:

• Proactive Threat Detection: Can identify threats that traditional methods might miss, such as new and emerging threats.

• Reduced Reliance on Signatures: Doesn’t rely on pre-defined signatures of known threats, which can be bypassed by new and evolving malware.

Anomaly analysis is like noticing someone behaving strangely at a concert. It helps identify unusual activity that might indicate a potential threat, even if you don’t know exactly what that threat.