The security controls and mechanisms that are in place must have a degree of transparency.
This enables the user to perform tasks and duties without having to go through extra steps because of the presence of the security controls. Transparency also does not let the user know too much about the controls, which helps prevent him from figuring out how to circumvent them. If the controls are too obvious, an attacker can figure out how to compromise them more easily.
Security (more specifically, the implementation of most security controls) has long been a sore point with users who are subject to security controls. Historically, security controls have been very intrusive to users, forcing them to interrupt their work flow and remember arcane codes or processes (like long passwords or access codes), and have generally been seen as an obstacle to getting work done. In recent years, much work has been done to remove that stigma of security controls as a detractor from the work process adding nothing but time and money. When developing access control, the system must be as transparent as possible to the end user. The users should be required to interact with the system as little as possible, and the process around using the control should be engineered so as to involve little effort on the part of the user.
For example, requiring a user to swipe an access card through a reader is an effective way to ensure a person is authorized to enter a room. However, implementing a technology (such as RFID) that will automatically scan the badge as the user approaches the door is more transparent to the user and will do less to impede the movement of personnel in a busy area.
In another example, asking a user to understand what applications and data sets will be required when requesting a system ID and then specifically requesting access to those resources may allow for a great deal of granularity when provisioning access, but it can hardly be seen as transparent. A more transparent process would be for the access provisioning system to have a role-based structure, where the user would simply specify the role he or she has in the organization and the system would know the specific resources that user needs to access based on that role. This requires less work and interaction on the part of the user and will lead to more accurate and secure access control decisions because access will be based on predefined need, not user preference.
When developing and implementing an access control system special care should be taken to ensure that the control is as transparent to the end user as possible and interrupts his work flow as little as possible.
The following answers were incorrect: All of the other detractors were incorrect.
Reference(s) used for this question:
HARRIS, Shon, All-In-One CISSP Certification Exam Guide, 6th edition. Operations Security, Page 1239-1240
Harris, Shon (2012-10-25). CISSP All-in-One Exam Guide, 6th Edition (Kindle Locations 25278-25281). McGraw-Hill. Kindle Edition.
Schneiter, Andrew (2013-04-15). Official (ISC)2 Guide to the CISSP CBK, Third Edition : Access Control ((ISC)2 Press) (Kindle Locations 713-729). Auerbach Publications. Kindle Edition.

Several analysis methods can be employed by an IDS, each with its own strengths and weaknesses, and their applicability to any given situation should be carefully considered.
There are two basic IDS analysis methods:
1. Pattern Matching (also called signature analysis), and 2. Anomaly detection
PATTERN MATCHING Some of the first IDS products used signature analysis as their detection method and simply looked for known characteristics of an attack (such as specific packet sequences or text in the data stream) to produce an alert if that pattern was detected. If a new or different attack vector is used, it will not match a known signature and, thus, slip past the IDS.
ANOMALY DETECTION Alternately, anomaly detection uses behavioral characteristics of a system’s operation or network traffic to draw conclusions on whether the traffic represents a risk to the network or host. Anomalies may include but are not limited to:
Multiple failed log-on attempts Users logging in at strange hours Unexplained changes to system clocks Unusual error messages Unexplained system shutdowns or restarts Attempts to access restricted files
An anomaly-based IDS tends to produce more data because anything outside of the expected behavior is reported. Thus, they tend to report more false positives as expected behavior patterns change. An advantage to anomaly-based IDS is that, because they are based on behavior identification and not specific patterns of traffic, they are often able to detect new attacks that may be overlooked by a signature-based system. Often information from an anomaly-based IDS may be used to create a pattern for a signature-based IDS.
Host Based Intrusion Detection (HIDS) HIDS is the implementation of IDS capabilities at the host level. Its most significant difference from NIDS is that related processes are limited to the boundaries of a single-host system. However, this presents advantages in effectively detecting objectionable activities because the IDS process is running directly on the host system, not just observing it from the network. This offers unfettered access to system logs, processes, system information, and device information, and virtually eliminates limits associated with encryption. The level of integration represented by HIDS increases the level of visibility and control at the disposal of the HIDS application.
Network Based Intrustion Detection (NIDS) NIDS are usually incorporated into the network in a passive architecture, taking advantage of promiscuous mode access to the network. This means that it has visibility into every packet traversing the network segment. This allows the system to inspect packets and monitor sessions without impacting the network or the systems and applications utilizing the network.
Below you have other ways that instrusion detection can be performed:
Stateful Matching Intrusion Detection Stateful matching takes pattern matching to the next level. It scans for attack signatures in the context of a stream of traffic or overall system behavior rather than the individual packets or discrete system activities. For example, an attacker may use a tool that sends a volley of valid packets to a targeted system. Because all the packets are valid, pattern matching is nearly useless. However, the fact that a large volume of the packets was seen may, itself, represent a known or potential attack pattern. To evade attack, then, the attacker may send the packets from multiple locations with long wait periods between each transmission to either confuse the signature detection system or exhaust its session timing window. If the IDS service is tuned to record and analyze traffic over a long period of time it may detect such an attack. Because stateful matching also uses signatures, it too must be updated regularly and, thus, has some of the same limitations as pattern matching.
Statistical Anomaly-Based Intrusion Detection The statistical anomaly-based IDS analyzes event data by comparing it to typical, known, or predicted traffic profiles in an effort to find potential security breaches. It attempts to identify suspicious behavior by analyzing event data and identifying patterns of entries that deviate from a predicted norm. This type of detection method can be very effective and, at a very high level, begins to take on characteristics seen in IPS by establishing an expected baseline of behavior and acting on divergence from that baseline. However, there are some potential issues that may surface with a statistical IDS. Tuning the IDS can be challenging and, if not performed regularly, the system will be prone to false positives. Also, the definition of normal traffic can be open to interpretation and does not preclude an attacker from using normal activities to penetrate systems. Additionally, in a large, complex, dynamic corporate environment, it can be difficult, if not impossible, to clearly define “normal” traffic. The value of statistical analysis is that the system has the potential to detect previously unknown attacks. This is a huge departure from the limitation of matching previously known signatures. Therefore, when combined with signature matching technology, the statistical anomaly-based IDS can be very effective.
Protocol Anomaly-Based Intrusion Detection A protocol anomaly-based IDS identifies any unacceptable deviation from expected behavior based on known network protocols. For example, if the IDS is monitoring an HTTP session and the traffic contains attributes that deviate from established HTTP session protocol standards, the IDS may view that as a malicious attempt to manipulate the protocol, penetrate a firewall, or exploit a vulnerability. The value of this method is directly related to the use of well-known or well-defined protocols within an environment. If an organization primarily uses well-known protocols (such as HTTP, FTP, or telnet) this can be an effective method of performing intrusion detection. In the face of custom or nonstandard protocols, however, the system will have more difficulty or be completely unable to determine the proper packet format. Interestingly, this type of method is prone to the same challenges faced by signature-based IDSs. For example, specific protocol analysis modules may have to be added or customized to deal with unique or new protocols or unusual use of standard protocols. Nevertheless, having an IDS that is intimately aware of valid protocol use can be very powerful when an organization employs standard implementations of common protocols.
Traffic Anomaly-Based Intrusion Detection A traffic anomaly-based IDS identifies any unacceptable deviation from expected behavior based on actual traffic structure. When a session is established between systems, there is typically an expected pattern and behavior to the traffic transmitted in that session. That traffic can be compared to expected traffic conduct based on the understandings of traditional system interaction for that type of connection. Like the other types of anomaly-based IDS, traffic anomaly-based IDS relies on the ability to establish “normal” patterns of traffic and expected modes of behavior in systems, networks, and applications. In a highly dynamic environment it may be difficult, if not impossible, to clearly define these parameters.
Reference(s) used for this question:
Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 3664-3686). Auerbach Publications. Kindle Edition. and Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 3711-3734). Auerbach Publications. Kindle Edition. and Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 3694-3711). Auerbach Publications. Kindle Edition.

There are two conceptual approaches to intrusion detection. Knowledge-based intrusion detection uses a database of known vulnerabilities to look for current attempts to exploit them on a system and trigger an alarm if an attempt is found. The other approach, not as common, is called behaviour-based or statistical analysis-based. A host-based intrusion detection system is a common implementation of intrusion detection, not a conceptual approach. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, John Wiley & Sons, 2001, Chapter 3: Telecommunications and Network Security (page 63). Also: HARRIS, Shon, All-In-One CISSP Certification Exam Guide, McGraw-Hill/Osborne, 2002, chapter 4: Access Control (pages 193-194).

If audit trails have been properly defined and implemented, they will record information that can assist in detecting intrusions. Source: HARRIS, Shon, All-In-One CISSP Certification Exam Guide, McGraw-Hill/Osborne, 2002, Chapter 4: Access Control (page 186).

A weakness of the signature-based (or knowledge-based) intrusion detection approach is that only attack signatures that are stored in a database are detected. Network-based intrusion detection can either be signature-based or statistical anomaly-based (also called behavior-based). Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, John Wiley & Sons, 2001, Chapter 2: Access control systems (page 49).

Controls provide accountability for individuals accessing information. Assurance procedures ensure that access control mechanisms correctly implement the security policy for the entire life cycle of an information system. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, John Wiley & Sons, 2001, Chapter 2: Access control systems (page 33).

There are two basic IDS analysis methods: pattern matching (also called signature analysis) and anomaly detection.
Anomaly detection uses behavioral characteristics of a system’s operation or network traffic to draw conclusions on whether the traffic represents a risk to the network or host. Anomalies may include but are not limited to:
Multiple failed log-on attempts Users logging in at strange hours Unexplained changes to system clocks Unusual error messages
The following are incorrect answers: Network-based ID Systems (NIDS) are usually incorporated into the network in a passive architecture, taking advantage of promiscuous mode access to the network. This means that it has visibility into every packet traversing the network segment. This allows the system to inspect packets and monitor sessions without impacting the network or the systems and applications utilizing the network.
Host-based ID Systems (HIDS) is the implementation of IDS capabilities at the host level. Its most significant difference from NIDS is that related processes are limited to the boundaries of a single-host system. However, this presents advantages in effectively detecting objectionable activities because the IDS process is running directly on the host system, not just observing it from the network. This offers unfettered access to system logs, processes, system information, and device information, and virtually eliminates limits associated with encryption. The level of integration represented by HIDS increases the level of visibility and control at the disposal of the HIDS application.
Signature Analysis Some of the first IDS products used signature analysis as their detection method and simply looked for known characteristics of an attack (such as specific packet sequences or text in the data stream) to produce an alert if that pattern was detected. For example, an attacker manipulating an FTP server may use a tool that sends a specially constructed packet. If that particular packet pattern is known, it can be represented in the form of a signature that IDS can then compare to incoming packets. Pattern-based IDS will have a database of hundreds, if not thousands, of signatures that are compared to traffic streams. As new attack signatures are produced, the system is updated, much like antivirus solutions. There are drawbacks to pattern-based IDS. Most importantly, signatures can only exist for known attacks. If a new or different attack vector is used, it will not match a known signature and, thus, slip past the IDS. Additionally, if an attacker knows that the IDS is present, he or she can alter his or her methods to avoid detection. Changing packets and data streams, even slightly, from known signatures can cause an IDS to miss the attack. As with some antivirus systems, the IDS is only as good as the latest signature database on the system.
For additional information on Intrusion Detection Systems –http://en.wikipedia.org/wiki/Intrusion_detection_system
Reference(s) used for this question: Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 3623-3625, 3649-3654, 3666-3686). Auerbach Publications. Kindle Edition.

Knowledge-based IDS are more common than behavior-based ID systems.
Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 63.
Application-Based IDS -“a subset of HIDS that analyze what’s going on in an application using the transaction log files of the application.” Source: Official ISC2 CISSP CBK Review Seminar Student Manual Version 7.0 p. 87
Host-Based IDS -“an implementation of IDS capabilities at the host level. Its most significant difference from NIDS is intrusion detection analysis, and related processes are limited to the boundaries of the host.” Source: Official ISC2 Guide to the CISSP CBK -p. 197
Network-Based IDS -“a network device, or dedicated system attached to the network, that monitors traffic traversing the network segment for which it is integrated.” Source: Official ISC2 Guide to the CISSP CBK -p. 196 CISSP for dummies a book that we recommend for a quick overview of the 10 domains has nice and concise coverage of the subject:
Intrusion detection is defined as real-time monitoring and analysis of network activity and data for potential vulnerabilities and attacks in progress. One major limitation of current intrusion detection system (IDS) technologies is the requirement to filter false alarms lest the operator (system or security administrator) be overwhelmed with data. IDSes are classified in many different ways, including active and passive, network-based and host-based, and knowledge-based and behavior-based: Active and passive IDS
An active IDS (now more commonly known as an intrusion prevention system — IPS) is a system that’s configured to automatically block suspected attacks in progress without any intervention required by an operator. IPS has the advantage of providing real-time corrective action in response to an attack but has many disadvantages as well. An IPS must be placed in-line along a network boundary; thus, the IPS itself is susceptible to attack. Also, if false alarms and legitimate traffic haven’t been properly identified and filtered, authorized users and applications may be improperly denied access. Finally, the IPS itself may be used to effect a Denial of Service (DoS) attack by intentionally flooding the system with alarms that cause it to block connections until no connections or bandwidth are available.
A passive IDS is a system that’s configured only to monitor and analyze network traffic activity and alert an operator to potential vulnerabilities and attacks. It isn’t capable of performing any protective or corrective functions on its own. The major advantages of passive IDSes are that these systems can be easily and rapidly deployed and are not normally susceptible to attack themselves. Network-based and host-based IDS
A network-based IDS usually consists of a network appliance (or sensor) with a Network Interface Card (NIC) operating in promiscuous mode and a separate management interface. The IDS is placed along a network segment or boundary and monitors all traffic on that segment.
A host-based IDS requires small programs (or agents) to be installed on individual systems to be monitored. The agents monitor the operating system and write data to log files and/or trigger alarms. A host-based IDS can only monitor the individual host systems on which the agents are installed; it doesn’t monitor the entire network. Knowledge-based and behavior-based IDS
A knowledge-based (or signature-based) IDS references a database of previous attack profiles and known system vulnerabilities to identify active intrusion attempts. Knowledge-based IDS is currently more common than behavior-based IDS.
Advantages of knowledge-based systems include the following:
It has lower false alarm rates than behavior-based IDS.
Alarms are more standardized and more easily understood than behavior-based IDS. Disadvantages of knowledge-based systems include these:
Signature database must be continually updated and maintained.
New, unique, or original attacks may not be detected or may be improperly classified.
A behavior-based (or statistical anomaly–based) IDS references a baseline or learned pattern of normal system activity to identify active intrusion attempts. Deviations from this baseline or pattern cause an alarm to be triggered.
Advantages of behavior-based systems include that they
Dynamically adapt to new, unique, or original attacks.
Are less dependent on identifying specific operating system vulnerabilities.
Disadvantages of behavior-based systems include
Higher false alarm rates than knowledge-based IDSes.
Usage patterns that may change often and may not be static enough to implement an effective behavior-based IDS.

Knowledge-based Intrusion Detection Systems use a database of previous attacks and known system vulnerabilities to look for current attempts to exploit their vulnerabilities, and trigger an alarm if an attempt is found. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 87.
Application-Based ID System -“a subset of HIDS that analyze what’s going on in an application using the transaction log files of the application.” Source: Official ISC2 CISSP CBK Review Seminar Student Manual Version 7.0 p. 87
Host-Based ID System -“an implementation of IDS capabilities at the host level. Its most significant difference from NIDS is intrusion detection analysis, and related processes are limited to the boundaries of the host.” Source: Official ISC2 Guide to the CISSP CBK -p. 197
Network-Based ID System -“a network device, or dedicated system attached to teh network, that monitors traffic traversing teh network segment for which it is integrated.” Source: Official ISC2 Guide to the CISSP CBK -p. 196

The two current conceptual approaches to Intrusion Detection methodology are knowledge-based ID systems and behavior-based ID systems, sometimes referred to as signature-based ID and statistical anomaly-based ID, respectively. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 63.