Event Parsing → Event parsing is the process of interpreting and normalizing raw event data from various sources into a consistent format.
Scenario: An organization receives logs from various devices (e.g., firewalls, routers, servers).
Action: Use a SIEM tool to parse and normalize these logs into a standardized format for easier analysis.
Event Duplication → Event duplication occurs when identical or similar events are recorded multiple times, leading to redundant data and potential alert fatigue.
Scenario: A firewall generates multiple identical alerts for the same incident.
Action: Configure SIEM rules to deduplicate these events and provide a single alert.
Non-Reporting Devices → Non-reporting devices are those that fail to send logs or event data to the SIEM system, potentially missing critical security information.
Scenario: A critical server stops sending logs to the SIEM system.
Action: Set up heartbeat monitoring to alert administrators when the server fails to report.
Retention → Retention refers to the period for which event data is stored within the SIEM system.
Scenario: An organization must retain event logs for seven years to comply with regulatory requirements.
Action: Configure SIEM retention policies to archive and store logs accordingly.
Event False Positives/False Negatives →
False Positives: Legitimate activity incorrectly flagged as a threat.
False Negatives: Malicious activity that goes undetected.
Scenario: An intrusion detection rule generates numerous false alerts for normal network traffic.
Action: Refine the rule to reduce false positives and accurately detect actual threats.
Correlation → Correlation involves linking related events across different sources and systems to identify patterns and detect complex threats.
Scenario: A user logs into the network from a foreign location, followed by multiple failed login attempts on various servers.
Action: Use correlation rules to link the login event with the failed attempts, triggering an alert for potential account compromise.
Audit Log Reduction → Audit log reduction involves filtering and summarizing logs to remove redundant or irrelevant data, making it easier to identify significant events.
Scenario: Thousands of routine system logs are generated daily, making it difficult to identify important events.
Action: Implement log filtering to exclude routine logs and summarize repetitive events.
Prioritization → Prioritization involves ranking events based on their potential impact and urgency to focus on the most critical incidents first.
Scenario: Multiple security alerts are generated, but resources are limited to address them all immediately.
Action: Use severity scoring to prioritize alerts based on their potential impact and urgency.
Trends → Identifying trends involves analyzing historical data to detect patterns and predict future security incidents.
Scenario: An increase in phishing emails is observed over the past few months.
Action: Perform trend analysis to identify the pattern and implement preventive measures.
Network Behavior Baselines → Establishing normal network activity patterns to detect unusual behaviors that may signify security threats.
Scenario: An increase in outbound traffic to an unknown external IP address is detected.
Action: Compare the current traffic with the baseline. If it deviates significantly, trigger an alert for potential data exfiltration.
System Behavior Baselines → Establishing normal operating patterns for systems to identify unusual activities that could indicate security issues.
Scenario: A sudden spike in CPU usage on a critical server is observed.
Action: Compare the spike with the system’s performance baseline to determine if it’s an anomaly, possibly indicating a DDoS attack or malware.
User Behavior Baselines → Establishing normal user activity patterns to detect anomalies that could indicate compromised accounts or insider threats.
Scenario: A user account is accessing sensitive data outside of normal working hours.
Action: Compare the access times with the established baseline. If it deviates significantly, investigate for potential account compromise.
Applications/Services Behavior Baselines → Establishing normal operating patterns for applications and services to detect unusual activities that could indicate security threats.
Scenario: An application experiences a sudden increase in error rates.
Action: Compare the error rates with the application’s baseline. If it deviates significantly, investigate for potential security issues such as exploitation attempts.
Third-Party Reports and Logs → Data and logs provided by external organizations, often including security reports, audit logs, and compliance assessments.
Threat Intelligence Feeds → Data streams that provide information about current threats, including indicators of compromise (IoCs) and tactics, techniques, and procedures (TTPs).
Vulnerability Scans → Automated scans that identify vulnerabilities in systems, applications, and networks
Common Vulnerabilities and Exposures (CVE) Details → A list of publicly disclosed information security vulnerabilities and exposures.
Bounty Programs → Programs that incentivize external researchers to find and report vulnerabilities in your systems.
Data Loss Prevention (DLP) Data → Data collected from DLP tools that monitor and protect sensitive information from unauthorized access and exfiltration.
Endpoint Logs → Logs collected from endpoints, including desktops, laptops, and mobile devices.
Infrastructure Device Logs → Logs from network devices such as routers, switches, firewalls, and load balancers.
Application Logs → Logs generated by applications, capturing detailed information about their operation and user interactions.
Cloud Security Posture Management (CSPM) Data → Data from CSPM tools that assess and monitor the security posture of cloud environments.
Visualization → The process of representing data in graphical or pictorial format to enhance understanding and analysis.
Dashboards → Interactive interfaces that display real-time data and metrics from various sources, providing an overview of the current security status.
Adversary Emulation Engagements → Simulating real-world attack techniques and tactics to evaluate the effectiveness of security controls and incident response capabilities.
Internal Reconnaissance → Gathering information from within the organization to identify potential vulnerabilities and areas of risk.
Hypothesis-Based Searches → Developing and testing hypotheses about potential threats based on available data and intelligence.
Honeypots → Deploying decoy systems designed to attract attackers, gather intelligence, and analyze attack techniques.
Honeynets → Creating a network of honeypots to simulate a larger, more complex environment for detecting and analyzing sophisticated threats.
User Behavior Analytics (UBA) → Analyzing user behavior patterns to detect anomalies that may indicate insider threats or compromised accounts.
Open-Source Intelligence (OSINT) → Gathering information from publicly available sources to identify potential threats and vulnerabilities.
Dark Web Monitoring → Monitoring the dark web for discussions, leaked data, and other information relevant to potential threats.
Information Sharing and Analysis Centers (ISACs) → Collaborating with industry-specific organizations that share threat intelligence and best practices.
Reliability Factors → Evaluating the trustworthiness and accuracy of external threat intelligence sources.
Counterintelligence → Actions and strategies designed to detect, prevent, and mitigate espionage and intelligence activities conducted by adversaries.
Operational Security (OpSec) → Processes and practices to protect information and activities from adversaries who might seek to exploit them.
Threat Intelligence Platforms (TIPs) and Third-Party Vendors#
Threat Intelligence Platforms (TIPs) → TIPs are tools designed to collect, aggregate, analyze, and disseminate threat intelligence data to improve an organization’s security posture.
TTPs describe the behaviors and methods used by adversaries to achieve their objectives. The MITRE ATT&CK Framework is a valuable resource for understanding TTPs.
Tactics: The high-level goals of an attacker (e.g., Initial Access, Execution).
Techniques: The methods used to achieve those goals (e.g., Phishing for Initial Access).
Procedures: The specific implementations of techniques used in attacks.
Volatile Storage Analysis → Refers to data that exists temporarily, such as RAM. Analyzing volatile storage provides real-time insights into system activities.
Techniques:
Memory Dump Analysis: Collecting and analyzing the contents of system memory.
Process Analysis: Identifying running processes, their states, and associated information.
Network Connections: Investigating open network connections and their endpoints.
Registry Analysis: Extracting and examining registry keys for information on system configuration and activities.
Non-Volatile Storage Analysis → Refers to data that persists after a system is powered off, such as hard drives or SSDs.
Techniques:
File System Analysis: Examining files, directories, and metadata.
Log File Analysis: Reviewing system and application logs.
Disk Forensics: Recovering deleted files and examining file system structures.
Email Header Analysis → Email headers contain metadata that provides information about the path an email took from sender to recipient, as well as technical details about the email’s origin and any intermediate servers.
Techniques:
Header Parsing: Extracting header fields such as Received, From, To, Subject, and Date.
Trace Email Path: Tracking the path of the email through different servers.
Identify Spoofing: Checking discrepancies in the From address or routing information.
Image Metadata Analysis → Image metadata can provide details about the creation, modification, and camera settings of an image.
Techniques:
EXIF Data Extraction: Extracting metadata such as camera make, model, and GPS coordinates.
Tamper Detection: Checking for signs of image manipulation.
GPS Information: Analyzing location data embedded in the image.
Audio/Video Metadata Analysis → Audio and video files contain metadata that can include information about the file’s creation, codec details, and modification history.
Techniques:
Extract Metadata: Reviewing details such as codec, duration, and bit rate.
Analyze Content: Checking for hidden or embedded data.
Verify Authenticity: Ensuring that the media file is genuine.
File/Filesystem Metadata Analysis → Analyzing the metadata of files and filesystems involves inspecting attributes like timestamps, file permissions, and file structure.
Techniques:
File Metadata Extraction: Reviewing file attributes such as creation and modification dates.
Filesystem Analysis: Examining filesystem structures for evidence of tampering or hidden files.
File Integrity Checking: Verifying that files have not been altered.
Joint Test Action Group (JTAG) → JTAG is a hardware debugging standard used for testing and programming hardware devices. It provides access to the internal states of a system’s components through a set of test access ports.
JTAG Setup for Incident Response:
Connecting to the Target Device: Attach a JTAG adapter to the device’s JTAG port.
Accessing the JTAG Interface: Use software tools to communicate with the target device via JTAG.
Extracting Data: Read the contents of memory, registers, and configuration settings.
Analyzing Hardware Components: Check for signs of tampering or unauthorized modifications.