Lateral Movement and Pivoting

The concepts of "lateral movement," "pivoting," and "privilege escalation" in cybersecurity explain how attackers use these techniques to navigate through a network, access different systems, and gain higher levels of access, often requires sophisticated detection methods like machine learning to identify suspicious activity amidst normal user behavior.

Key points:

Lateral movement:

This refers to an attacker moving from one compromised system to another within a network to reach their target data or system, often by exploiting shared credentials or vulnerabilities.

Pivoting:

Similar to lateral movement, pivoting involves using an initially compromised system as a launchpad to access other systems within the network, essentially "hopping" from one compromised machine to another to penetrate the network further.

Privilege escalation:

Once an attacker gains initial access to a system, they may attempt to elevate their user privileges to gain administrative control, allowing them to perform more sensitive actions.

PtH (Pass the Hash) attacks help facilitate these types of attacks.

Detection challenges:

Normal vs. anomalous behavior:

Differentiating between legitimate user actions and malicious activity can be complex, making detection reliant on advanced techniques like machine learning algorithms to identify unusual behavior patterns.

Anomalous logins and privilege use:

Monitoring for suspicious logins from unusual locations, excessive failed login attempts, or sudden elevation of user privileges can indicate potential lateral movement or privilege escalation attempts.

Impossible Travel Time

 Impossible Travel

"Impossible travel" in cybersecurity means a user is attempting to access an account from two geographically distant locations within a timeframe that is too short to realistically travel between them, suggesting a potential security breach where someone else is using the account from a different location than the legitimate user.

Key points about "impossible travel":

Anomaly detection:

An anomaly detection method analyzes user logins based on their geographical location to identify suspicious activity.

How it works:

If a user logs in from New York and then a few minutes later from London, it triggers an "impossible travel" alert because it's impossible to physically travel between the two cities that quickly.

Indicator of compromise:

This can be an early indicator that a malicious actor has compromised a user's account.

Factors considered:

Security systems look at the time difference between logins, the distance between locations, and the user's typical login patterns to determine if "impossible travel" is occurring.

SCAP (Security Content Automation Protocol)

 Security Content Automation Protocol

The most critical components of SCAP (Security Content Automation Protocol) that enable vulnerability scanners to determine if a computer meets a configuration baseline are Extensible Configuration Checklist Description Format (XCCDF) which defines security policies and checks, and Open Vulnerability and Assessment Language (OVAL) which provides the technical details on how to perform those checks on a system, along with Common Platform Enumeration (CPE) for identifying specific software and hardware platforms.

Key points about these components:

XCCDF:

This format specifies the high-level security requirements and configuration checks, mapping policies to technical tests.

OVAL:

This language details how to perform the checks defined in XCCDF on a specific system, including the steps to verify compliance.

CPE:

This component provides a standardized way to identify software and hardware components on a system, allowing for accurate vulnerability assessment

 Flow Collector

A "flow collector" is a network monitoring tool that gathers aggregated information about network traffic ("metadata" like source/destination IP addresses, port numbers, byte counts, etc.) from various network devices like switches, routers, and firewalls, instead of capturing every individual packet, allowing for analysis of overall traffic patterns and trends rather than detailed inspection of each frame, which is particularly useful for identifying anomalies, malicious activity, and application usage patterns on a network.

Key points about flow collectors:

Collects metadata, not complete packets:

Unlike traditional packet capture tools, a flow collector only records key details about each network flow, significantly reducing the amount of data needed to be stored and analyzed.

Multiple sources:

Flow data can be collected from various network devices, such as switches, routers, firewalls, and web proxies, providing a comprehensive view of network traffic.

Flow analysis capabilities:

Once collected, specialized tools can analyze flow data to identify trends, anomalies, and potential security threats based on factors like application usage, traffic volume, source/destination IP addresses, and port numbers.

Benefits:

Performance optimization: Flow collectors can efficiently handle high-volume network traffic by only collecting metadata.

Network visibility: Provides a holistic view of network activity, allowing administrators to identify unusual traffic patterns and potential issues.

Security insights: This can help detect malicious activity like malware communication, tunneling, and unauthorized applications.

Capacity planning: Identifying network bottlenecks and optimizing bandwidth allocation based on application usage.

Example features of a flow analysis tool:

Application identification:

Identifying which applications are generating the most traffic on the network.

Traffic visualization:

Displaying network connections graphically to quickly see how data flows between different devices

Alerting capabilities:

Generating notifications when specific traffic patterns or anomalies are detected, like excessive traffic from a particular IP address or unusual port activity

Custom reporting:

Creating reports based on specific criteria to monitor network usage and identify potential issues

 NetFlow and sFlow

NetFlow and sFlow are network monitoring technologies that provide insights into network traffic and performance. The main differences between the two are:

Approach

sFlow samples packets at the interface level, while NetFlow statefully tracks flows.

Accuracy

sFlow uses randomization, while NetFlow can record and track all incoming sessions.

Compatibility

sFlow is vendor-neutral and compatible with many networking equipment, while NetFlow was developed by Cisco and is designed for use on Cisco's Internet Operating System (IOS).

Flexibility

NetFlow allows administrators to enable or disable sampling based on network needs, while sFlow inherently relies on sampling.

Here are some other differences between sFlow and NetFlow:

Data captured

sFlow captures deeper levels of information than NetFlow, including full packet headers and partial packet payloads.

Scalability

sFlow can be a more scalable option in very high-speed networks because the network device has no flow cache.

Exporting

sFlow exports records incompatible with NetFlow, but many network monitoring and analysis tools support both formats.

 Metadata

Metadata refers to information about data itself, like when a file was created, who created it, or where it was stored. It essentially provides context and details about the data without revealing its actual content; in cybersecurity investigations, this metadata attached to logged events and files can be crucial for establishing timelines and identifying potential breach origins by showing "when" and "where" actions occurred.

Key points about metadata:

What it describes:

Metadata provides details about a data file's origin, properties, and history, including the creation date, modification date, author, file size, and permissions.

File system tracking:

Operating systems automatically record file metadata, such as creation, access, and modification timestamps, which can be valuable for forensic analysis.

Security attributes:

Files can have additional metadata like read-only, hidden, or system file flags, indicating security settings applied to them.

Extended attributes:

Beyond basic file system metadata, files might contain extended attributes like author names, copyright information, or tags for easier searching.

Relevance in investigations:

By analyzing metadata, investigators can build a timeline of events, pinpoint potential breach sources, and identify suspicious activity based on when and where files were accessed or modified.

Example of how metadata is used in investigations:

Identifying malicious activity: If a critical system file is suddenly modified at an unusual time, the metadata (timestamp) could indicate a potential intrusion attempt.

Tracking file movement: Investigators can determine when and from which system a copied file was transferred by examining its metadata.

Identifying the source of a document: Metadata, such as author information on a document, can help trace its origin.

 Security Control Categories

Security controls protect a system or data asset by ensuring confidentiality, integrity, availability, and non-repudiation. Depending on how they are implemented, these controls can be categorized as managerial, operational, technical, or physical. Examples include risk assessments (managerial), security guard patrols (operational), firewalls (technical), and security cameras (physical).

Key points:

Confidentiality: Limiting access to information to authorized users only.

Integrity: Ensuring data is accurate and not tampered with.

Availability: Guaranteeing that information is accessible to authorized users when needed.

Non-repudiation: Preventing a user from denying their actions on a system.

Control categories:

Managerial:

Policies, procedures, risk assessments, and oversight functions performed by management.

Operational:

Actions taken by users and system administrators, like security awareness training and access control procedures.

Technical:

Hardware and software mechanisms like firewalls, encryption, and access control systems.

Physical:

Physical security measures include locks, alarms, cameras, mantraps, access control vestibule, turnstiles, and site access controls.

Example controls in each category:

Managerial: Security policy document, risk management process, vendor assessment

Operational: User access reviews, password management procedures, incident response plan

Technical: Intrusion detection system, antivirus, port security, 802.1x, least privilege using group policy, data encryption, antivirus software

Physical: Building access control system, security cameras, data center environmental controls