Securing Networks · ⁨네트워크 보안⁩

Learning Objective 3.1.A: Identify common network attacks.

• 3.1.A.1 The address resolution protocol (ARP) is used by a default gateway on a network to establish a table that pairs internet protocol (IP) addresses with media access control (MAC) addresses. An ARP poisoning attack is when an adversary sends falsified ARP packets to the default gateway to modify the table so that the adversary’s device receives traffic intended for the target by linking the target’s IP address to the adversary’s MAC address. Faking a MAC address is called MAC spoofing. This is an example of an on-path attack (or man-in-the-middle attack), which is when an adversary interrupts a data stream between two parties, captures both parties’ data, and copies or alters the data before sending them on. Both parties think they are communicating directly with each other, but instead they are each communicating with the adversary who is secretly intercepting their messages.
• 3.1.A.2 A MAC flooding attack is when an adversary sends the target switch many Ethernet frames, each with a different MAC address. This can force the switch into broadcast mode, and the adversary can then collect all of the frames on the network (because they are being broadcast), which could allow the adversary to access sensitive information. This is an example of eavesdropping (or sniffing), which is when an adversary captures data in transit and can record and copy the data.
• 3.1.A.3 A domain name system (DNS) poisoning attack is when an adversary pretends to be an authoritative name server (NS) and plants a fake DNS record on a DNS server to redirect browser traffic to a malicious website designed to steal credentials. This is an example of credential harvesting, which is when adversaries set up a fake login site that looks like a real one. Unsuspecting users enter their real credentials, which the adversaries capture and use.
• 3.1.A.4 A smurf attack attempts to overwhelm a network with Internet Control Message Protocol (ICMP) requests. It is a type of denial of service (DoS) attack, which makes a system or resource unavailable to authorized users. During a smurf attack, an adversary sends many ICMP requests with the victim’s address to the network’s broadcast address. The network’s gateway then sends these requests to all devices on the network. Each device on the network replies to the victim’s address, creating a flood of traffic that can block legitimate messages. When multiple devices attack the same target simultaneously, it’s called a distributed denial of service (DDoS) attack.

Learning Objective 3.1.B: Explain how adversaries can exploit network vulnerabilities to steal, disrupt, or destroy network communication.

• 3.1.B.1 Adversaries can send malicious traffic into a network to flood it creating a DoS, to map the internal structure of the network, or to spoof a legitimate device. Networks without firewalls, or with improperly configured firewalls, are vulnerable to these types of attacks.
• 3.1.B.2 Adversaries that have compromised a device often attempt to leverage their access to compromise other devices on the local area network (LAN).
• 3.1.B.3 Adversaries that physically plug into a data port can gain access to a LAN through the switch port unless port security is enabled. This allows adversaries to launch DoS attacks or perform MAC flooding or MAC spoofing attacks.
• 3.1.B.4 Adversaries standing outside of physically secure spaces can pick up the signals and beacon frames from a wireless access point that is broadcasting outside the physical space. This allows them to gather information about the wireless network and to attempt eavesdropping and cryptographic attacks on it.
• 3.1.B.5 Adversaries can attempt to join networks to launch attacks from within the networks. Networks that do not authenticate devices and users make it easier for adversaries to join.
• 3.1.B.6 If there is an open network port, an adversary can plug a wireless access point into the port creating a rogue access point. The adversary could use this rogue access point to access the internal network wirelessly (maybe even from outside the physical space). This allows the adversary direct access to the LAN, bypassing any firewalls.
• 3.1.B.7 Adversaries can attempt to break wireless encryption and intercept, steal, or compromise data on a network.

Learning Objective 3.1.C: Assess and document risks from network vulnerabilities.

• 3.1.C.1 Vulnerabilities on a network can lead to adversaries being able to intercept and alter data in transit, launch DoS attacks, or move laterally on a network to gain access to more sensitive or critical systems. Network vulnerabilities can constitute a risk to confidentiality, integrity, and availability.
• 3.1.C.2 There are automated vulnerability scanners that can check networks, devices, and applications for known vulnerabilities. These scanners produce a report that often includes the vulnerabilities detected, their severity, and mitigation recommendations.
• 3.1.C.3 Successfully exploiting a network vulnerability often requires advanced technical ability and knowledge. This can impact the likelihood of an exploit.
• 3.1.C.4 High risks from network vulnerabilities allow an adversary to easily have a significant impact by capturing network traffic, spoofing a legitimate device on the network, or launching a DoS attack.
  • Illustrative examples for 3.1.C.4:
    • An organization has a single unsegmented internal network that is accessible via a wireless network with weak encryption, and on that network it has a server running its proprietary web-application.
• 3.1.C.5 Moderate risks from network vulnerabilities could include vulnerabilities that might give adversaries the ability to gain information about systems or devices on a network.
  • Illustrative examples for 3.1.C.5:
    • An organization’s external firewall is not configured to block external ICMP traffic.
• 3.1.C.6 Low risks from network vulnerabilities include vulnerabilities that would be difficult to exploit and would likely have minimal negative impacts on an organization.
  • Illustrative examples for 3.1.C.6:
    • An organization has wireless access points that broadcast a beacon frame, which contains the network service set identifier (SSID) and the wireless encryption protocols.

Learning Objective 3.2.A: Identify managerial controls related to network security.

• 3.2.A.1 A router security policy will set forth a minimum configuration standard for routers on an organization’s network and may include:
  • Banning local user accounts (All router logins must use an approved authentication server.)
  • Disabling unnecessary services (e.g., Telnet)
  • Requiring a firewall (An organization may opt for a firewall device separate from the router.)
• 3.2.A.2 A switch security policy will set forth a minimum configuration standard for switches on an organization’s network and may include:
  • Banning local user accounts (All switch logins must use an approved authentication server.)
  • Requiring port security to be enabled.
  • Using MAC filtering
• 3.2.A.3 A virtual private network (VPN) policy will detail the minimum security requirements for employees using a VPN to access an organization’s internal network, and it may include:
  • A list of roles within the organization that are allowed to use a VPN to access the organization’s internal network
  • Authentication requirements for employees using a VPN (e.g., public/private key system or MFA)
  • A prohibition against split tunneling (also called dual tunneling)
• 3.2.A.4 A wireless security policy will establish the minimum security requirements for wireless networks within an organization and may include:
  • Requiring users to authenticate to the wireless network through an extensible authentication protocol (EAP) connected to an approved authentication server
  • Requiring all wireless traffic to be encrypted using AES encryption with a minimum key length
  • Disabling beacon frames on wireless access points

Learning Objective 3.2.B: Configure wireless network security features.

• 3.2.B.1 Organizations can disable beacon frame broadcasting on wireless access points (WAPs) to make it harder for adversaries to find their wireless network and learn its basic properties.
• 3.2.B.2 Organizations can control the broadcast direction and signal strength of a WAP so the signal does not extend beyond the physical space the access point is meant to cover.
• 3.2.B.3 Organizations should enable strong wireless encryption protocols to ensure wireless frames are not readable by adversaries who might intercept them.
  • WEP, WPS, and the original WPA wireless encryption protocols have known vulnerabilities and are insecure.
  • WPA3 is currently the strongest wireless encryption algorithm.
• 3.2.B.4 Organizations can enable MAC filtering to prevent unauthorized devices from accessing the network, and they can require users to authenticate when joining a network.

Learning Objective 3.3.A: Identify techniques for segmenting a network.

• 3.3.A.1 Firewall zones and rules can be used to create a screened subnet (also known as a demilitarized zone, or DMZ)—a network segment that sits between public, external networks like the internet and internal, private networks. A screened subnet is typically a lower security zone than the internal, private networks, and it typically holds an organization’s publicly facing resources, separating them from the internal network.
• 3.3.A.2 Subnetting can be used to create different subnets based on IP addressing. If a device is compromised by an adversary, subnets can contain a security breach to reduce the number of exposed devices.
• 3.3.A.3 Switches can be used to create VLANs, which logically separate devices physically connected to central switches.

Learning Objective 3.3.B: Explain why network segmentation can increase network security.

• 3.3.B.1 Network segmentation refers to the process of dividing a network into smaller, isolated segments or subnetworks (subnets).
• 3.3.B.2 Dividing a network into smaller subnets isolates network traffic, which can prevent attacks on one subnet from impacting devices on other subnets.
• 3.3.B.3 Network segmentation can allow for different security policies and controls to be applied to different segments of the network, allowing for higher security zones and lower security zones.
• 3.3.B.4 Port security on a switch can prevent MAC flooding by limiting the number of addresses assignable to any single switch port.

Learning Objective 3.5.A: Identify types of automated security tools used to detect network attacks.

• 3.5.A.1 Automated detection tools analyze data collected from an organization’s network and devices, such as switches and routers, servers, firewalls, and user computers. These data are often collected in a log file.
• 3.5.A.2 A network intrusion detection system (NIDS) is an automated tool that analyzes data to determine if malicious activity is taking place on a network. When an attack is detected, it generates an alert.
• 3.5.A.3 A network intrusion prevention system (NIPS) is an automated tool that, like an IDS, analyzes data to determine if malicious activity is taking place on a network. A NIPS can also mitigate or halt an attack by closing ports, blocking specific IP or MAC addresses, or rejecting specific protocols.
• 3.5.A.4 A security information and event management (SIEM) system collects and analyzes data from multiple sources (including firewalls, NIDS/NIPS, device logs, and application logs) to detect patterns that may indicate a cyberattack and raises an alert if a potential attack is detected. Security analysts investigate the alert to determine whether it represents a true threat and follow standard operating procedures to resolve or escalate the alert.

Learning Objective 3.5.B: Explain how organizations can leverage artificial intelligence (AI) to enhance threat detection and response.

• 3.5.B.1 Computers log every action that users take. Firewalls, IDS, IPS, and other network sensors log all the traffic passing through various points in a network. A medium-sized organization’s network is logging millions (or even tens of millions) of data points per day. Even a large team of humans is incapable of analyzing so much data.
• 3.5.B.2 Threat detection teams are creating AI algorithms to analyze large amounts of data and classify the data patterns as malicious or normal.
• 3.5.B.3 AI models for threat detection are based on probabilistic calculations; they report a percentage to indicate the likelihood that something is malicious.
• 3.5.B.4 Organizations determine their own thresholds for what percentage of likelihood of a threat results in an alert. If the threshold is set too high, real attacks may go undetected; if the threshold is too low, the security team will be overwhelmed with false alerts.

Learning Objective 3.5.C: Determine a network detection method.

• 3.5.C.1 Volume of network traffic is a criterion for determining a detection method. Signature-based detection is more efficient for networks with high traffic volume. Signature-based detection compares detection data to a database of known indicators of compromise (IoCs), called signatures. Signature databases must be updated with IoCs for the latest attacks. Signature-based detection runs more quickly than anomaly-based detection.
• 3.5.C.2 Consistency of network traffic patterns is a criterion for determining a detection method. Anomaly-based detection is most effective on networks with consistent traffic patterns. Anomaly-based detection compares detection data to a baseline of recorded activity. Baselines must be recorded on uncompromised systems to establish expected data types and volumes. Anomaly-based detection triggers an alert or action when data types or volumes outside of a specified tolerance range are recorded. Anomaly-based detection relies on consistent patterns in network traffic to detect anomalous traffic patterns.
• 3.5.C.3 Degree of sensitivity or criticality of a network is a criterion for determining a detection method. Networks with more sensitive or critical data or services will likely consider a hybrid approach. Hybrid detection combines signature-based and anomaly-based detection. Hybrid detection is more expensive than using either signature- or anomaly-based detection alone, and hybrid-detection models generate more alerts.
• 3.5.C.4 Likelihood of novel attacks on a network is a criterion for determining a detection method. Signature-based detection cannot detect a new attack. When an organization suspects that adversaries are likely to attempt a new attack on a network, anomaly-based detection is the preferred method when the cost of hybrid detection is prohibitively high.

Learning Objective 3.5.D: Evaluate the impact of a network detection method.

• 3.5.D.1 Speed of detection is a factor in evaluating the impact of a network detection method. Faster detection enables faster response. Signature-based detection methods are faster than anomaly-based detection methods, especially on networks with high traffic volume.
• 3.5.D.2 Cost is a factor in evaluating the impact of a network detection method. Detection tools and ongoing costs need to be within a budget. Anomaly-based detection systems require more expensive hardware to operate than signature based. Hybrid detection is the most expensive option because it combines both anomaly- and signature-based methods.
• 3.5.D.3 False positive rate is a factor in evaluating the impact of a network detection method. Signature-based detection has almost no false positives. Anomaly-based or hybrid detection will have higher false positive rates. Impacts of high false positive rates include:
  • Time and resources are put toward investigating alerts for nonmalicious activity.
  • Alert fatigue is a condition that occurs when responders get accustomed to false positives and take alerts less seriously because they assume alerts are false positives before investigating them.
• 3.5.D.4 False negative rate is a factor in evaluating the impact of a network detection method. A false negative occurs when an adversary can bypass a detection system. Signature-based detection systems are easier to bypass than anomaly-based or hybrid systems. False negatives can result in adversaries causing loss, harm, disruption, or destruction to data and systems.

Learning Objective 3.5.E: Apply detection techniques to identify indicators of network attacks by analyzing log files.

• 3.5.E.1 Evil-twin attacks can be detected by regularly scanning for service set identifiers (SSIDs) that look suspicious or similar to local legitimate SSIDs. Signal triangulation can be used to locate and disable an access point broadcasting an evil-twin network.
• 3.5.E.2 Jamming attacks can be detected by recognizing that no wireless devices in a specific physical space are able to connect to a wireless network and by scanning for electromagnetic (EM) noise in the wireless range.
• 3.5.E.3 ARP poisoning attacks can be detected by monitoring network traffic for unusual ARP messages (particularly duplicate MAC address ARP packets) and checking the ARP table on the default gateway.
• 3.5.E.4 MAC flooding attacks can be detected by monitoring network traffic for an unexpected surge of Ethernet frames with different MAC addresses and checking the MAC address table on a switch.
• 3.5.E.5 DNS poisoning attacks are difficult to detect. However, if an organization’s website experiences an abrupt and otherwise inexplicable drop in traffic, DNS records should be examined as a potential cause.
• 3.5.E.6 Smurf attacks can be detected by watching network traffic for a sudden increase in ICMP requests sent to the network’s broadcast address.
• 3.5.E.7 Network-based IoCs are discovered when analyzing network traffic, often in the form of packet capture files. Indicators can be found in source and destination IP addresses, ports, and protocols. These can include:
  • Connections to known malicious IP addresses
  • Unauthorized network scans
  • Unusual spikes or slow downs in network traffic
  • Mismatched port-application traffic