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Chapter: Cryptography and Network Security

Intrusion Detection

Inevitably, the best intrusion prevention system will fail. A system's second line of defense is intrusion detection, and this has been the focus of much research in recent years.



Inevitably, the best intrusion prevention system will fail. A system's second line of defense is intrusion detection, and this has been the focus of much research in recent years. This interest is motivated by a number of considerations, including the following:


·        If an intrusion is detected quickly enough, the intruder can be identified and ejected from the system before any damage is done or any data are compromised.


·        An effective intrusion detection system can serve as a deterrent, so acting to prevent intrusions.


·        Intrusion detection enables the collection of information about intrusion techniques that can be used to strengthen the intrusion prevention facility.


Intrusion detection is based on the assumption that the behavior of the intruder differs from that of a legitimate user in ways that can be quantified.


Figure 5.2.1 suggests, in very abstract terms, the nature of the task confronting the designer of an intrusion detection system. Although the typical behavior of an intruder differs from the typical behavior of an authorized user, there is an overlap in these behaviors. Thus, a loose interpretation of intruder behavior, which will catch more intruders, will also lead to a number of "false positives," or authorized users identified as intruders. On the other hand, an attempt to limit false positives by a tight interpretation of intruder behavior will lead to an increase in false negatives, or intruders not identified as intruders. Thus, there is an element of compromise and art in the practice of intrusion detection.



1. The approaches to intrusion detection:


Statistical anomaly detection: Involves the collection of data relating to the behavior of legitimate users over a period of time. Then statistical tests are applied to observed behavior to determine with a high level of confidence whether that behavior is not legitimate user behavior.


Threshold detection: This approach involves defining thresholds, independent of user, for the frequency of occurrence of various events.


Profile based: A profile of the activity of each user is developed and used to detect changes in the behavior of individual accounts.


j) Rule-based detection: Involves an attempt to define a set of rules that can be used to decide that a given behavior is that of an intruder.


Anomaly detection: Rules are developed to detect deviation from previous usage patterns.


Penetration identification: An expert system approach that searches for suspicious behavior.


In terms of the types of attackers listed earlier, statistical anomaly detection is effective against masqueraders. On the other hand, such techniques may be unable to deal with misfeasors. For such attacks, rule-based approaches may be able to recognize events and sequences that, in context, reveal penetration. In practice, a system may exhibit a combination of both approaches to be effective against a broad range of attacks.


Audit Records


A fundamental tool for intrusion detection is the audit record. Some record of ongoing activity by users must be maintained as input to an intrusion detection system. Basically, two plans are used:


·        Native audit records: Virtually all multiuser operating systems include accounting software that collects information on user activity. The advantage of using this information is that no additional collection software is needed. The disadvantage is that the native audit records

·        may not contain the needed information or may not contain it in a convenient form.  


·        Detection-specific audit records: A collection facility can be implemented that generates audit records containing only that information required by the intrusion detection system. One advantage of such an approach is that it could be made vendor independent and ported to a variety of systems. The disadvantage is the extra overhead involved in having, in effect, two accounting packages running on a machine.  


Each audit record contains the following fields:


·        Subject: Initiators of actions. A subject is typically a terminal user but might also be a


o   process acting on behalf of users or groups of users.  


·        Object: Receptors of actions. Examples include files, programs, messages, records, terminals, printers, and user- or program-created structures


·        7.       Resource-Usage: A list of quantitative elements in which each element gives the amount used of some resource (e.g., number of lines printed or displayed, number of records read

o   or written, processor time, I/O units used, session elapsed time).  



·        8. Time-Stamp: Unique time-and-date stamp identifying when the action took place.   Most user operations are made up of a number of elementary actions. For example, a file copy involves the execution of the user command, which includes doing access validation and setting up the copy, plus the read from one file, plus the write to another file. Consider the command




issued by Smith to copy an executable file GAME from the current directory to the <Library> directory. The following audit records may be generated:



In this case, the copy is aborted because Smith does not have write permission to <Library>. The decomposition of a user operation into elementary actions has three advantages:


Because objects are the protectable entities in a system, the use of elementary actions enables an audit of all behavior affecting an object. Thus, the system can detect attempted subversions of access


Single-object, single-action audit records simplify the model and the implementation.


Because of the simple, uniform structure of the detection-specific audit records, it may be relatively easy to obtain this information or at least part of it by a straightforward mapping from existing native audit records to the detection-specific audit records.


1.1 Statistical Anomaly Detection:


As was mentioned, statistical anomaly detection techniques fall into two broad categories: threshold detection and profile-based systems. Threshold detection involves counting the number of occurrences of a specific event type over an interval of time. If the count surpasses what is considered a reasonable number that one might expect to occur, then intrusion is assumed.


Threshold analysis, by itself, is a crude and ineffective detector of even moderately sophisticated attacks. Both the threshold and the time interval must be determined.


1.2 Profile-based anomaly detection focuses on characterizing the past behavior of individual users or related groups of users and then detecting significant deviations. A profile may consist of a set of parameters, so that deviation on just a single parameter may not be sufficient in itself to signal an alert.


The foundation of this approach is an analysis of audit records. The audit records provide input to the intrusion detection function in two ways. First, the designer must decide on a number of quantitative metrics that can be used to measure user behavior. Examples of metrics that are useful for profile-based intrusion detection are the following:


·        Counter: A nonnegative integer that may be incremented but not decremented until it is reset by management action. Typically, a count of certain event types is kept over a particular period of time. Examples include the number of logins by a single user during an hour, the number of times a given command is executed during a single user session, and the number of password failures during a minute.  


·        Gauge: A nonnegative integer that may be incremented or decremented. Typically, a gauge is used to measure the current value of some entity. Examples include the number of logical connections assigned to a user application and the number of outgoing messages queued for a user process.


·        Interval timer: The length of time between two related events. An example is the length of time between successive logins to an account.  


·        Resource utilization: Quantity of resources consumed during a specified period. Examples include the number of pages printed during a user session and total time consumed by a program execution.  


Given these general metrics, various tests can be performed to determine whether current activity fits within acceptable limits.


·      Mean and standard deviation

·       Multivariate  

·       Markov process  

·       Time series  

·       Operational  


The simplest statistical test is to measure the mean and standard deviation of a parameter over some historical period. This gives a reflection of the average behavior and its variability.


A multivariate model is based on correlations between two or more variables. Intruder behavior may be characterized with greater confidence by considering such correlations (for example, processor time and resource usage, or login frequency and session elapsed time).


A Markov process model is used to establish transition probabilities among various states. As an example, this model might be used to look at transitions between certain commands.


A time series model focuses on time intervals, looking for sequences of events that happen too rapidly or too slowly. A variety of statistical tests can be applied to characterize abnormal timing.


Finally, an operational model is based on a judgment of what is considered abnormal, rather than an automated analysis of past audit records. Typically, fixed limits are defined and intrusion is suspected for an observation that is outside the limits.


1.3 Rule-Based Intrusion Detection


Rule-based techniques detect intrusion by observing events in the system and applying a set of rules that lead to a decision regarding whether a given pattern of activity is or is not suspicious.


Rule-based anomaly detection is similar in terms of its approach and strengths to statistical anomaly detection. With the rule-based approach, historical audit records are analyzed to identify usage patterns and to generate automatically rules that describe those patterns. Rules may represent past behavior patterns of users, programs, privileges, time slots, terminals, and so on. Current behavior is then observed, and each transaction is matched against the set of rules to determine if it conforms to any historically observed pattern of behavior.


As with statistical anomaly detection, rule-based anomaly detection does not require knowledge of security vulnerabilities within the system. Rather, the scheme is based on observing past behavior and, in effect, assuming that the future will be like the past


Rule-based penetration identification takes a very different approach to intrusion detection, one based on expert system technology. The key feature of such systems is the use of rules for identifying known penetrations or penetrations that would exploit known weaknesses.

Example heuristics are the following:


o   Users should not read files in other users' personal directories.


o   Users must not write other users' files.


o   Users who log in after hours often access the same files they used earlier.


o   Users do not generally open disk devices directly but rely on higher-level operating system utilities.


o   Users should not be logged in more than once to the same system.


o   Users do not make copies of system programs.


2 The Base-Rate Fallacy


To be of practical use, an intrusion detection system should detect a substantial percentage of intrusions while keeping the false alarm rate at an acceptable level. If only a modest percentage of actual intrusions are detected, the system provides a false sense of security. On the other hand, if the system frequently triggers an alert when there is no intrusion (a false alarm), then either system managers will begin to ignore the alarms, or much time will be wasted analyzing the false alarms.


Unfortunately, because of the nature of the probabilities involved, it is very difficult to meet the standard of high rate of detections with a low rate of false alarms. In general, if the actual numbers of intrusions is low compared to the number of legitimate uses of a system, then the false alarm rate will be high unless the test is extremely discriminating.


3 Distributed Intrusion Detection


Until recently, work on intrusion detection systems focused on single-system stand-alone facilities. The typical organization, however, needs to defend a distributed collection of hosts supported by a LAN Porras points out the following major issues in the design of a distributed intrusion detection system


A distributed intrusion detection system may need to deal with different audit record formats. In a heterogeneous environment, different systems will employ different native audit collection systems and, if using intrusion detection, may employ different formats for security-related audit records.  


One or more nodes in the network will serve as collection and analysis points for the data from the systems on the network. Thus, either raw audit data or summary data must be transmitted across the network. Therefore, there is a requirement to assure the integrity and confidentiality of these data.  


Either a centralized or decentralized architecture can be used.


Below figure shows the overall architecture, which consists of three main components:


·        Host agent module: An audit collection module operating as a background process on a monitored system. Its purpose is to collect data on security-related events on the host and transmit these to the central manager.  


·        LAN monitor agent module: Operates in the same fashion as a host agent module except that it analyzes LAN traffic and reports the results to the central manager.  



·        Central manager module: Receives reports from LAN monitor and host agents and processes and correlates these reports to detect intrusion.  


The scheme is designed to be independent of any operating system or system auditing implementation.


·        The agent captures each audit record produced by the native audit collection system.

·        A filter is applied that retains only those records that are of security interest.

·        These records are then reformatted into a standardized format referred to as the host audit record (HAR).

·        Next, a template-driven logic module analyzes the records for suspicious activity.

·        At the lowestlevel, the agent scans for notable events that are of interest independent of any        past events.

·        Examplesinclude failed file accesses, accessing system files, and changing a file's access control.

·        At the next higher level, the agent looks for sequences of events, such as known attack atterns (signatures).

·        Finally, the agent looks for anomalous behavior of an individual user based on a historical profile of that user, such as number of programs executed, number of files accessed, and the like.

·        When suspicious activity is detected, an alert is sent to the central manager.

·        The central manager includes an expert system that can draw inferences from received data.

·        The manager may also query individual systems for copies of HARs to correlate with those from other agents.

·        The LAN monitor agent also supplies information to the central manager.

·        The LAN monitor agent audits host-host connections, services used, and volume of traffic.

·        It  searches  for  significant  events,  such  as  sudden  changes in network  load,  the  use  of

·        security-related services, and network activities such as rlogin.


The architecture is quite general and flexible. It offers a foundation for a machine-independent approach that can expand from stand-alone intrusion detection to a system that is able to correlate activity from a number of sites and networks to detect suspicious activity that would otherwise remain undetected.


4 Honeypots


A relatively recent innovation in intrusion detection technology is the honeypot. Honeypots are decoy systems that are designed to lure a potential attacker away from critical systems. Honeypots are designed to


·       divert an attacker from accessing critical systems  


·        collect information about the attacker's activity  


·        encourage the attacker to stay on the system long enough for administrators to respond


These systems are filled with fabricated information designed to appear valuable but that a legitimate user of the system wouldn't access. Thus, any access to the honeypot is suspect.


5 Intrusion Detection Exchange Format


To facilitate the development of distributed intrusion detection systems that can function across a wide range of platforms and environments, standards are needed to support interoperability. Such standards are the focus of the IETF Intrusion Detection Working Group.


The outputs of this working group include the following:


a.     A requirements document, which describes the high-level functional requirements for communication between intrusion detection systems and with management systems, including the rationale for those requirements.


b.     A common intrusion language specification, which describes data formats that satisfy the requirements.


c.      A framework document, which identifies existing protocols best used for communication between intrusion detection systems, and describes how the devised data formats relate to them.



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