Exfiltration Over Alternative Protocol:  Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

APT28 has exfiltrated archives of collected data previously staged on a target's OWA server via HTTPS.(Citation: Cybersecurity Advisory GRU Brute Force Campaign July 2021)

IcedID has exfiltrated collected data via HTTPS.(Citation: DFIR_Sodinokibi_Ransomware)

CURIUM has used SMTPS to exfiltrate collected data from victims.(Citation: PWC Yellow Liderc 2023)

APT29 has exfiltrated collected data over a simple HTTPS request to a password-protected archive staged on a victim's OWA servers.(Citation: Volexity SolarWinds)

Rclone can exfiltrate data over SFTP or HTTPS via WebDAV.(Citation: Rclone)

UNC2452 exfiltrated collected data over a simple HTTPS request to a password-protected archive staged on a victim's OWA servers.(Citation: Volexity SolarWinds)

Storm-1811 has exfiltrated captured user credentials via Secure Copy Protocol (SCP).(Citation: rapid7-email-bombing)

During the SolarWinds Compromise, APT29 exfiltrated collected data over a simple HTTPS request to a password-protected archive staged on a victim's OWA servers.(Citation: Volexity SolarWinds)

Use intrusion detection signatures to block traffic at network boundaries.

Network segmentation involves dividing a network into smaller, isolated segments to control and limit the flow of traffic between devices, systems, and applications. By segmenting networks, organizations can reduce the attack surface, restrict lateral movement by adversaries, and protect critical assets from compromise. Effective network segmentation leverages a combination of physical boundaries, logical separation through VLANs, and access control policies enforced by network appliances like firewalls, routers, and cloud-based configurations. This mitigation can be implemented through the following measures: Segment Critical Systems: - Identify and group systems based on their function, sensitivity, and risk. Examples include payment systems, HR databases, production systems, and internet-facing servers. - Use VLANs, firewalls, or routers to enforce logical separation. Implement DMZ for Public-Facing Services: - Host web servers, DNS servers, and email servers in a DMZ to limit their access to internal systems. - Apply strict firewall rules to filter traffic between the DMZ and internal networks. Use Cloud-Based Segmentation: - In cloud environments, use VPCs, subnets, and security groups to isolate applications and enforce traffic rules. - Apply AWS Transit Gateway or Azure VNet peering for controlled connectivity between cloud segments. Apply Microsegmentation for Workloads: - Use software-defined networking (SDN) tools to implement workload-level segmentation and prevent lateral movement. Restrict Traffic with ACLs and Firewalls: - Apply Access Control Lists (ACLs) to network devices to enforce "deny by default" policies. - Use firewalls to restrict both north-south (external-internal) and east-west (internal-internal) traffic. Monitor and Audit Segmented Networks: - Regularly review firewall rules, ACLs, and segmentation policies. - Monitor network flows for anomalies to ensure segmentation is effective. Test Segmentation Effectiveness: - Perform periodic penetration tests to verify that unauthorized access is blocked between network segments.

Employ network appliances and endpoint software to filter ingress, egress, and lateral network traffic. This includes protocol-based filtering, enforcing firewall rules, and blocking or restricting traffic based on predefined conditions to limit adversary movement and data exfiltration. This mitigation can be implemented through the following measures: Ingress Traffic Filtering: - Use Case: Configure network firewalls to allow traffic only from authorized IP addresses to public-facing servers. - Implementation: Limit SSH (port 22) and RDP (port 3389) traffic to specific IP ranges. Egress Traffic Filtering: - Use Case: Use firewalls or endpoint security software to block unauthorized outbound traffic to prevent data exfiltration and command-and-control (C2) communications. - Implementation: Block outbound traffic to known malicious IPs or regions where communication is unexpected. Protocol-Based Filtering: - Use Case: Restrict the use of specific protocols that are commonly abused by adversaries, such as SMB, RPC, or Telnet, based on business needs. - Implementation: Disable SMBv1 on endpoints to prevent exploits like EternalBlue. Network Segmentation: - Use Case: Create network segments for critical systems and restrict communication between segments unless explicitly authorized. - Implementation: Implement VLANs to isolate IoT devices or guest networks from core business systems. Application Layer Filtering: - Use Case: Use proxy servers or Web Application Firewalls (WAFs) to inspect and block malicious HTTP/S traffic. - Implementation: Configure a WAF to block SQL injection attempts or other web application exploitation techniques.

Data Loss Prevention (DLP) involves implementing strategies and technologies to identify, categorize, monitor, and control the movement of sensitive data within an organization. This includes protecting data formats indicative of Personally Identifiable Information (PII), intellectual property, or financial data from unauthorized access, transmission, or exfiltration. DLP solutions integrate with network, endpoint, and cloud platforms to enforce security policies and prevent accidental or malicious data leaks. (Citation: PurpleSec Data Loss Prevention) This mitigation can be implemented through the following measures: Sensitive Data Categorization: - Use Case: Identify and classify data based on sensitivity (e.g., PII, financial data, trade secrets). - Implementation: Use DLP solutions to scan and tag files containing sensitive information using predefined patterns, such as Social Security Numbers or credit card details. Exfiltration Restrictions: - Use Case: Prevent unauthorized transmission of sensitive data. - Implementation: Enforce policies to block unapproved email attachments, unauthorized USB usage, or unencrypted data uploads to cloud storage. Data-in-Transit Monitoring: - Use Case: Detect and prevent the transmission of sensitive data over unapproved channels. - Implementation: Deploy network-based DLP tools to inspect outbound traffic for sensitive content (e.g., financial records or PII) and block unapproved transmissions. Endpoint Data Protection: - Use Case: Monitor and control sensitive data usage on endpoints. - Implementation: Use endpoint-based DLP agents to block copy-paste actions of sensitive data and unauthorized printing or file sharing. Cloud Data Security: - Use Case: Protect data stored in cloud platforms. - Implementation: Integrate DLP with cloud storage platforms like Google Drive, OneDrive, or AWS to monitor and restrict sensitive data sharing or downloads.