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For many organizations, the most challenging part of the incident response process is accurately detecting and assessing possible incidents—determining whether an incident has occurred and, if so, the type, extent, and magnitude of the problem. What makes this so challenging is a combination of three factors.

  • Incidents may be detected through many different means, with varying levels of detail and fidelity. 
  • Automated detection capabilities include network-based and host-based IDPSs, antivirus software,  and log analyzers. Incidents may also be detected through manual means, such as problems reported by users. Some incidents have overt signs that can be easily detected, whereas others are almost impossible to detect.
  • The volume of potential signs of incidents is typically high—for example, it is not uncommon for an organization to receive thousands or even millions of intrusion detection sensor alerts per day.
  • Deep, specialized technical knowledge and extensive experience are necessary for proper and efficient analysis of incident-related data.
  • Signs of an incident fall into one of two categories: precursors and indicators. A precursor is a sign that an incident may occur in the future. An indicator is a sign that an incident may have occurred or may be occurring now.
  • Most attacks do not have any identifiable or detectable precursors from the target’s perspective.
  • If precursors are detected, the organization may have an opportunity to prevent the incident by altering its security posture to save a target from attack.

At a minimum, the organization could monitor activity involving the target more closely. Examples of precursors are:

  • Web server log entries that show the usage of a vulnerability scanner
  • An announcement of a new exploit that targets a vulnerability of the organization’s mail server
  • A threat from a group stating that the group will attack the organization.

While precursors are relatively rare, indicators are all too common. Too many types of indicators exist to exhaustively list them, but some examples are listed below.

  • A network intrusion detection sensor alerts when a buffer overflow attempt occurs against a database server.
  • Antivirus software alerts when it detects that a host is infected with malware. <
  • A system administrator sees a filename with unusual characters.
  • A host records an auditing configuration change in its log. 
  • An application logs multiple failed login attempts from an unfamiliar remote system.
  • An email administrator sees a large number of bounced emails with suspicious content.
  • A network administrator notices an unusual deviation from typical network traffic flows.


Precursors and indicators are identified using many different sources, with the most common being computer security software alerts, logs, publicly available information, and people.


IDPSs. IDPS products identify suspicious events and record pertinent data regarding them, including the date and time the attack was detected, the type of attack, the source and destination IP  addresses, and the username (if applicable and known). Most IDPS products use attack signatures to identify malicious activity; the signatures must be kept up to date so that the newest attacks can be detected. IDPS software often produces false positives—alerts that indicate malicious activity is occurring, when in fact there has been none. Analysts should manually validate IDPS alerts either by closely reviewing the recorded supporting data or by getting related data from other sources.

SIEMs. Security Information and Event Management (SIEM) products are similar to IDPS products, but they generate alerts based on analysis of log data.

Antivirus and antispam software. Antivirus software detects various forms of malware, generates alerts, and prevents the malware from infecting hosts. Current antivirus products are effective at stopping many instances of malware if their signatures are kept up to date. Antispam software is used to detect spam and prevent it from reaching users’ mailboxes. Spam may contain malware, phishing attacks, and other malicious content, so alerts from antispam software may indicate attack attempts.

File integrity checking software. File integrity checking software can detect changes made to important files during incidents. It uses a hashing algorithm to obtain a cryptographic checksum for each designated file. If the file is altered and the checksum is recalculated, an extremely high probability exists that the new checksum will not match the old checksum. By regularly recalculating checksums and comparing them with previous values, changes to files can be detected.

Third-party monitoring services. Third parties offer a variety of subscription-based and free monitoring services. An example is fraud detection services that will notify an organization if its IP addresses, domain names, etc. are associated with current incident activity involving other organizations. There are also free real-time blacklists with similar information. Another example of a third-party monitoring service is a CSIRC notification list; these lists are often available only to other incident response teams.


Operating system, service, and application logs. Logs from operating systems, services, and applications (particularly audit-related data) are frequently of great value when an incident occurs, such as recording which accounts were accessed and what actions were performed. Organizations should require a baseline level of logging on all systems and a higher baseline level on critical systems. Logs can be used for analysis by correlating event information. Depending on the event information, an alert can be generated to indicate an incident.

Network device logs. Logs from network devices such as firewalls and routers are not typically a primary source of precursors or indicators. Although these devices are usually configured to log blocked connection attempts, they provide little information about the nature of the activity. Still, they can be valuable in identifying network trends and in correlating events detected by other devices.

Network flows. A network flow is a particular communication session occurring between hosts. Routers and other networking devices can provide network flow information, which can be used to find anomalous network activity caused by malware, data exfiltration, and other malicious acts. There are many standards for flow data formats, including NetFlow, sFlow, and IPFIX.


Information on new vulnerabilities and exploits. Keeping up with new vulnerabilities and exploits can prevent some incidents from occurring and assist in detecting and analyzing new attacks. The National Vulnerability Database (NVD) contains information on vulnerabilities.


People from within the organization. Users, system administrators, network administrators, security staff, and others from within the organization may report signs of incidents. It is important to validate all such reports. One approach is to ask people who provide such information how confident they are of the accuracy of the information. Recording this estimate along with the information provided can help considerably during incident analysis, particularly when conflicting data is discovered.

People from other organizations. Reports of incidents that originate externally should be taken seriously. For example, the organization might be contacted by a party claiming a system at the organization is attacking its systems. External users may also report other indicators, such as a defaced web page or an unavailable service. Other incident response teams also may report incidents. It is important to have mechanisms in place for external parties to report indicators and for trained staff to monitor those mechanisms carefully; this may be as simple as setting up a phone number and email address, configured to forward messages to the help desk.

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