Intrusion Detection & Prevention Systems : The Ultimate Guide

Intrusion Detection and Prevention Systems

Intrusion Detection and Prevention System Principles

Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices.

Incidents have many causes, such as malware (e.g., worms, viruses), attackers gaining unauthorized access to systems from the Internet, and authorized users of systems who misuse their privileges or attempt to gain additional privileges for which they are not authorized. Although many incidents are malicious in nature, many others are not; for example, a person might mistype the address of a computer and accidentally attempt to connect to a different system without authorization.

An intrusion detection system (IDS) is software that automates the intrusion detection process. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents.

IDS and IPS technologies offer many of the same capabilities, and administrators can usually disable prevention features in IPS products, causing them to function as IDSs. Accordingly, for brevity the term intrusion detection and prevention systems (IDPS) is used here to refer to both IDS and IPS technologies. Any exceptions are specifically noted.

Uses of Intrusion Detection and Prevention Systems

IDPSs are primarily focused on identifying possible incidents. For example, an IDPS could detect when an attacker has successfully compromised a system by exploiting a vulnerability in the system. The IDPS could then report the incident to security administrators, who could quickly initiate incident response actions to minimize the damage caused by the incident.

The IDPS could also log information that could be used by the incident handlers. Many IDPSs can also be configured to recognize violations of security policies. For example, some IDPSs can be configured with firewall ruleset-like settings, allowing them to identify network traffic that violates the organization’s security or acceptable use policies. Also, some IDPSs can monitor file transfers and identify ones that might be suspicious, such as copying a large database onto a user’s laptop.

Many IDPSs can also identify reconnaissance activity, which may indicate that an attack is imminent. For example, some attack tools and forms of malware, particularly worms, perform reconnaissance activities such as host and port scans to identify targets for subsequent attacks. An IDPS might be able to block reconnaissance and notify security administrators, who can take actions if needed to alter other security controls to prevent related incidents. Because reconnaissance activity is so frequent on the Internet, reconnaissance detection is often performed primarily on protected internal networks.

In addition to identifying incidents and supporting incident response efforts, organizations have found other uses for IDPSs, including the following:

  • Identifying security policy problems. An IDPS can provide some degree of quality control for security policy implementation, such as duplicating firewall rulesets and alerting when it sees network traffic that should have been blocked by the firewall but was not because of a firewall configuration error.
  • Documenting the existing threat to an organization. IDPSs log information about the threats that they detect. Understanding the frequency and characteristics of attacks against an organization’s computing resources is helpful in identifying the appropriate security measures for protecting the resources. The information can also be used to educate management about the threats that the organization faces.
  • Deterring individuals from violating security policies. If individuals are aware that their actions are being monitored by IDPS technologies for security policy violations, they may be less likely to commit such violations because of the risk of detection.

Because of the increasing dependence on information systems and the prevalence and potential impact of intrusions against those systems, IDPSs have become a necessary addition to the security infrastructure of nearly every organization.

Key Functions of Intrusion Detection and Prevention Systems

There are many types of IDPS technologies, which are differentiated primarily by the types of events that they can recognize and the methodologies that they use to identify incidents. In addition to monitoring and analyzing events to identify undesirable activity, all types of IDPS technologies typically perform the following functions:

  • Recording information related to observed events. Information is usually recorded locally, and might also be sent to separate systems such as centralized logging servers, security information and event management (SIEM) solutions, and enterprise management systems.
  • Notifying security administrators of important observed events. This notification, known as an alert, occurs through any of several methods, including the following: emails, pages, messages on the IDPS user interface, Simple Network Management Protocol (SNMP) traps, syslog messages, and user-defined programs and scripts. A notification message typically includes only basic information regarding an event; administrators need to access the IDPS for additional information.
  • Producing reports. Reports summarize the monitored events or provide details on particular events of interest.

Some IDPSs are also able to change their security profile when a new threat is detected. For example, an IDPS might be able to collect more detailed information for a particular session after malicious activity is detected within that session. An IDPS might also alter the settings for when certain alerts are triggered or what priority should be assigned to subsequent alerts after a particular threat is detected.

IPS technologies are differentiated from IDS technologies by one characteristic: IPS technologies can respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which can be divided into the following groups:

  • The IPS stops the attack itself. Examples of how this could be done are as follows:

    • Terminate the network connection or user session that is being used for the attack
    • Block access to the target (or possibly other likely targets) from the offending user account, IP address, or other attacker attribute
    • Block all access to the targeted host, service, application, or other resource.
  • The IPS changes the security environment. The IPS could change the configuration of other security controls to disrupt an attack. Common examples are reconfiguring a network device (e.g., firewall, router, switch) to block access from the attacker or to the target, and altering a host-based firewall on a target to block incoming attacks. Some IPSs can even cause patches to be applied to a host if the IPS detects that the host has vulnerabilities.

  • The IPS changes the attack’s content. Some IPS technologies can remove or replace malicious portions of an attack to make it benign. A simple example is an IPS removing an infected file attachment from an email and then permitting the cleaned email to reach its recipient. A more complex example is an IPS that acts as a proxy and normalizes incoming requests, which means that the proxy repackages the payloads of the requests, discarding header information. This might cause certain attacks to be discarded as part of the normalization process.

Another common attribute of IDPS technologies is that they cannot provide completely accurate detection.

When an IDPS incorrectly identifies benign activity as being malicious, a false positive has occurred. When an IDPS fails to identify malicious activity, a false negative has occurred.

It is not possible to eliminate all false positives and negatives; in most cases, reducing the occurrences of one increases the occurrences of the other. Many organizations choose to decrease false negatives at the cost of increasing false positives, which means that more malicious events are detected but more analysis resources are needed to differentiate false positives from true malicious events.

Altering the configuration of an IDPS to improve its detection accuracy is known as tuning.

Most IDPS technologies also offer features that compensate for the use of common evasion techniques.

Evasion is modifying the format or timing of malicious activity so that its appearance changes but its effect is the same. Attackers use evasion techniques to try to prevent IDPS technologies from detecting their attacks. For example, an attacker could encode text characters in a particular way, knowing that the target understands the encoding and hoping that any monitoring IDPSs do not. Most IDPS technologies can overcome common evasion techniques by duplicating special processing performed by the targets.

If the IDPS can “see” the activity in the same way that the target would, then evasion techniques will generally be unsuccessful at hiding attacks.

Common Detection Methodologies

IDPS technologies use many methodologies to detect incidents. Most IDPS technologies use multiple detection methodologies, either separately or integrated, to provide more broad and accurate detection.

Signature-Based Detection

A signature is a pattern that corresponds to a known threat. Signature-based detection is the process of comparing signatures against observed events to identify possible incidents.

5 Examples of signatures are as follows:

  • A telnet attempt with a username of “root”, which is a violation of an organization’s security policy
  • An email with a subject of “Free pictures!” and an attachment filename of “freepics.exe”, which are characteristics of a known form of malware
  • An operating system log entry with a status code value of 645, which indicates that the host’s auditing has been disabled.

Signature-based detection is very effective at detecting known threats but largely ineffective at detecting previously unknown threats, threats disguised by the use of evasion techniques, and many variants of known threats. For example, if an attacker modified the malware in the previous example to use a filename of “freepics2.exe”, a signature looking for “freepics.exe” would not match it.

Signature-based detection is the simplest detection method because it just compares the current unit of activity, such as a packet or a log entry, to a list of signatures using string comparison operations.

Signature-based detection technologies have little understanding of many network or application protocols and cannot track and understand the state of complex communications. For example, they cannot pair a request with the corresponding response, such as knowing that a request to a web server for a particular page generated a response status code of 403, meaning that the server refused to fill the request. They also lack the ability to remember previous requests when processing the current request.

This limitation prevents signature-based detection methods from detecting attacks that comprise multiple events if none of the events contains a clear indication of an attack.

Anomaly-Based Detection

Anomaly-based detection is the process of comparing definitions of what activity is considered normal against observed events to identify significant deviations.

An IDPS using anomaly-based detection has profiles that represent the normal behavior of such things as users, hosts, network connections, or applications. The profiles are developed by monitoring the characteristics of typical activity over a period of time. For example, a profile for a network might show that email activity comprises an average of 13% of network bandwidth at the Internet border during typical workday hours. The IDPS then uses statistical methods to compare the characteristics of current activity to thresholds related to the profile, such as detecting when email activity comprises significantly more bandwidth than expected and alerting an administrator of the anomaly.

Profiles can be developed for many behavioral attributes, such as the number of web pages visited by a user, the number of failed login attempts for a host, and the level of processor usage for a host in a given period of time. The major benefit of anomaly-based detection methods is that they can be very effective at detecting previously unknown threats. For example, suppose that a computer becomes infected with a new type of malware. The malware could consume the computer’s processing resources, send large numbers of emails, initiate large numbers of network connections, and perform other behavior that would be significantly different from the established profiles for the computer.

An initial profile is generated over a period of time (typically days, sometimes weeks) sometimes called a training period. Profiles for anomaly-based detection can either be static or dynamic. Once generated, a static profile is unchanged unless the IDPS is specifically directed to generate a new profile. A dynamic profile is adjusted constantly as additional events are observed. Because systems and networks change over time, the corresponding measures of normal behavior also change; a static profile will eventually become inaccurate, so it needs to be regenerated periodically. Dynamic profiles do not have this problem, but they are susceptible to evasion attempts from attackers. For example, an attacker can perform small amounts of malicious activity occasionally, then slowly increase the frequency and quantity of activity. If the rate of change is sufficiently slow, the IDPS might think the malicious activity is normal behavior and include it in its profile.

Malicious activity might also be observed by an IDPS while it builds its initial profiles.

Inadvertently including malicious activity as part of a profile is a common problem with anomaly-based IDPS products. (In some cases, administrators can modify the profile to exclude activity in the profile that is known to be malicious.) Another problem with building profiles is that it can be very challenging in some cases to make them accurate, because computing activity can be so complex. For example, if a particular maintenance activity that performs large file transfers occurs only once a month, it might not be observed during the training period; when the maintenance occurs, it is likely to be considered a significant deviation from the profile and trigger an alert. Anomaly-based IDPS products often produce many false positives because of benign activity that deviates significantly from profiles, especially in more diverse or dynamic environments.

Another noteworthy problem with the use of anomaly-based detection techniques is that it is often difficult for analysts to determine why a particular alert was generated and to validate that an alert is accurate and not a false positive, because of the complexity of events and number of events that may have caused the alert to be generated.

Stateful Protocol Analysis

Stateful protocol analysis is the process of comparing predetermined profiles of generally accepted definitions of benign protocol activity for each protocol state against observed events to identify deviations.

Unlike anomaly-based detection, which uses host or network-specific profiles, stateful protocol analysis relies on vendor-developed universal profiles that specify how particular protocols should and should not be used.

The “stateful” in stateful protocol analysis means that the IDPS is capable of understanding and tracking the state of network, transport, and application protocols that have a notion of state. For example, when a user starts a File Transfer Protocol (FTP) session, the session is initially in the unauthenticated state. Unauthenticated users should only perform a few commands in this state, such as viewing help information or providing usernames and passwords.

An important part of understanding state is pairing requests with responses, so when an FTP authentication attempt occurs, the IDPS can determine if it was successful by finding the status code in the corresponding response. Once the user has authenticated successfully, the session is in the authenticated state, and users are expected to perform any of several dozen commands. Performing most of these commands while in the unauthenticated state would be considered suspicious, but in the authenticated state performing most of them is considered benign.

Stateful protocol analysis can identify unexpected sequences of commands, such as issuing the same command repeatedly or issuing a command without first issuing a command upon which it is dependent.

Another state tracking feature of stateful protocol analysis is that for protocols that perform authentication, the IDPS can keep track of the authenticator used for each session, and record the authenticator used for suspicious activity. This is helpful when investigating an incident. Some IDPSs can also use the authenticator information to define acceptable activity differently for multiple classes of users or specific users.

The “protocol analysis” performed by stateful protocol analysis methods usually includes reasonableness checks for individual commands, such as minimum and maximum lengths for arguments. If a command typically has a username argument, and usernames have a maximum length of 20 characters, then an argument with a length of 1000 characters is suspicious. If the large argument contains binary data, then it is even more suspicious.

Stateful protocol analysis methods use protocol models, which are typically based primarily on protocol standards from software vendors and standards bodies (e.g., Internet Engineering Task Force [IETF] Request for Comments [RFC]).

The protocol models also typically take into account variances in each protocol’s implementation. Many standards are not exhaustively complete in explaining the details of the protocol, which causes variations among implementations. Also, many vendors either violate standards or add proprietary features, some of which may replace features from the standards.

For proprietary protocols, complete details about the protocols are often not available, making it difficult for IDPS technologies to perform comprehensive, accurate analysis. As protocols are revised and vendors alter their protocol implementations, IDPS protocol models need to be updated to reflect those changes.

The primary drawback to stateful protocol analysis methods is that they are very resource-intensive because of the complexity of the analysis and the overhead involved in performing state tracking for many simultaneous sessions.

Another serious problem is that stateful protocol analysis methods cannot detect attacks that do not violate the characteristics of generally acceptable protocol behavior, such as performing many benign actions in a short period of time to cause a denial of service.

Yet another problem is that the protocol model used by an IDPS might conflict with the way the protocol is implemented in particular versions of specific applications and operating systems, or how different client and server implementations of the protocol interact.

Types of Intrusion Detection and Prevention Systems

There are many types of IDPS technologies. They are divided into the following four groups based on the type of events that they monitor and the ways in which they are deployed:

  • Network-Based, which monitors network traffic for particular network segments or devices and analyzes the network and application protocol activity to identify suspicious activity. It can identify many different types of events of interest. It is most commonly deployed at a boundary between networks, such as in proximity to border firewalls or routers, virtual private network (VPN) servers, remote access servers, and wireless networks.
  • Wireless, which monitors wireless network traffic and analyzes its wireless networking protocols to identify suspicious activity involving the protocols themselves. It cannot identify suspicious activity in the application or higher-layer network protocols (e.g., TCP, UDP) that the wireless network traffic is transferring. It is most commonly deployed within range of an organization’s wireless network to monitor it, but can also be deployed to locations where unauthorized wireless networking could be occurring.
  • Network Behavior Analysis (NBA), which examines network traffic to identify threats that generate unusual traffic flows, such as distributed denial of service (DDoS) attacks, certain forms of malware (e.g., worms, backdoors), and policy violations (e.g., a client system providing network services to other systems). NBA systems are most often deployed to monitor flows on an organization’s internal networks, and are also sometimes deployed where they can monitor flows between an organization’s networks and external networks (e.g., the Internet, business partners’ networks).
  • Host-Based, which monitors the characteristics of a single host and the events occurring within that host for suspicious activity. Examples of the types of characteristics a host-based IDPS might monitor are network traffic (only for that host), system logs, running processes, application activity, file access and modification, and system and application configuration changes. Host-based IDPSs are most commonly deployed on critical hosts such as publicly accessible servers and servers containing sensitive information.

Some forms of Intrusion Detection and Prevention Systems are more mature than others because they have been in use much longer.

Networkbased IDPS and some forms of host-based IDPS have been commercially available for over fifteen years.

Network behavior analysis software is a somewhat newer form of IDPS that evolved in part from products created primarily to detect DDoS attacks, and in part from products developed to monitor traffic flows on internal networks.

Wireless technologies are a comparatively new type of IDPS, developed in response to the popularity of wireless local area networks (WLAN) and the growing threats against WLANs and WLAN clients.


Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices.

Intrusion prevention is the process of performing intrusion detection and attempting to stop detected possible incidents.

Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators. In addition, organizations use IDPSs for other purposes, such as identifying problems with security policies, documenting existing threats, and deterring individuals from violating security policies.

IDPSs have become a necessary addition to the security infrastructure of nearly every organization.

There are many types of IDPS technologies, which are differentiated primarily by the types of events that they can recognize and the methodologies that they use to identify possible incidents.

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