Intrusion detection system

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This article is about the computing term. For other uses, see Burglar alarm.

An Intrusion detection system (IDS) is software and/or hardware designed to detect unwanted attempts at accessing, manipulating, and/or disabling of computer systems, mainly through a network, such as the Internet. These attempts may take the form of attacks, as examples, by crackers, malware and/or disgruntled employees. An IDS cannot directly detect attacks within properly encrypted traffic.

An intrusion detection system is used to detect several types of malicious behaviors that can compromise the security and trust of a computer system. This includes network attacks against vulnerable services, data driven attacks on applications, host based attacks such as privilege escalation, unauthorized logins and access to sensitive files, and malware (viruses, trojan horses, and worms).

An IDS can be composed of several components: Sensors which generate security events, a Console to monitor events and alerts and control the sensors, and a central Engine that records events logged by the sensors in a database and uses a system of rules to generate alerts from security events received. There are several ways to categorize an IDS depending on the type and location of the sensors and the methodology used by the engine to generate alerts. In many simple IDS implementations all three components are combined in a single device or appliance.

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[edit] IDS Terminology

Alert/Alarm- A signal suggesting a system has been or is being attacked [1].

True attack stimulus- An event that triggers an IDS to produce an alarm and react as though a real attack were in progress [1].

False attack stimulus- The event signaling an IDS to produce an alarm when no attack has taken place [1].

False (False Positive)- An alert or alarm that is triggered when no actual attack has taken place [1].

False negative- A failure of an IDS to detect an actual attack [1].

Noise- Data or interference that can trigger a false positive [1].

Site policy- Guidelines within an organization that control the rules and configurations of an IDS [1].

Site policy awareness- The ability an IDS has to dynamically change its rules and configurations in response to changing environmental activity [1].

Confidence value- A value an organization places on an IDS based on past performance and analysis to help determine its ability to effectively identify an attack [1].

Alarm filtering- The process of categorizing attack alerts produced from an IDS in order to distinguish false positives from actual attacks [1].


[edit] Types of Intrusion-Detection systems

In a network-based intrusion-detection system (NIDS), the sensors are located at choke points in network to be monitored, often in the demilitarized zone (DMZ) or at network borders. The sensor captures all network traffic and analyzes the content of individual packets for malicious traffic. In systems, PIDS and APIDS are used to monitor the transport and protocols for illegal or inappropriate traffic or constructs of a language (say SQL). In a host-based system, the sensor usually consists of a software agent, which monitors all activity of the host on which it is installed. Hybrids of these two systems also exist.

  • A protocol-based intrusion detection system (PIDS) consists of a system or agent that would typically sit at the front end of a server, monitoring and analyzing the communication protocol between a connected device (a user/PC or system) and the server. For a web server this would typically monitor the HTTPS protocol stream and understand the HTTP protocol relative to the web server/system it is trying to protect. Where HTTPS is in use then this system would need to reside in the "shim", or interface, between where HTTPS is un-encrypted and immediately prior to its entering the Web presentation layer.
  • An application protocol-based intrusion detection system (APIDS) consists of a system or agent that would typically sit within a group of servers, monitoring and analyzing the communication on application specific protocols. For example, in a web server with a database this would monitor the SQL protocol specific to the middleware/business logic as it transacts with the database.
  • A host-based intrusion detection system (HIDS) consists of an agent on a host which identifies intrusions by analyzing system calls, application logs, file-system modifications (binaries, password files, capability/acl databases) and other host activities and state. An example of a HIDS is OSSEC.
  • A hybrid intrusion detection system combines two or more approaches. Host agent data is combined with network information to form a comprehensive view of the network. An example of a Hybrid IDS is Prelude.

[edit] Passive system vs. reactive system

In a passive system, the intrusion detection system (IDS) sensor detects a potential security breach, logs the information and signals an alert on the console and or owner. In a reactive system, also known as an intrusion prevention system (IPS), the IDS responds to the suspicious activity by resetting the connection or by reprogramming the firewall to block network traffic from the suspected malicious source. This can happen automatically or at the command of an operator.

Though they both relate to network security, an intrusion detection system (IDS) differs from a firewall in that a firewall looks outwardly for intrusions in order to stop them from happening. Firewalls limit access between networks to prevent intrusion and do not signal an attack from inside the network. An IDS evaluates a suspected intrusion once it has taken place and signals an alarm. An IDS also watches for attacks that originate from within a system. This is traditionally achieved by examining network communications, identifying heuristics and patterns (often known as signatures) of common computer attacks, and taking action to alert operators. A system which terminates connections is called an intrusion prevention system, and is another form of an application layer firewall.

The term IDPS is commonly used to refer to hybrid security systems that both "detect" and "prevent".

[edit] Statistical anomaly and signature based IDSes

All Intrusion Detection Systems use one of two detection techniques: statistical anomaly based and/or signature based.

Statistical anomaly based IDS- A statistical anomaly based IDS establishes a performance baseline based on normal network traffic evaluations. It will then sample current network traffic activity to this baseline in order to detect whether or not it is within baseline parameters. If the sampled traffic is outside baseline parameters an alarm will be triggered [1].

Signature based IDS- Network traffic is examined for preconfigured and predetermined attack patterns known as signatures. Many attacks today have distinct signatures. In good security practice, a collection of these signatures must be constantly updated to mitigate emerging threats [1].

[edit] Limitations

Noise-Noise can severely limit an IDS’s effectiveness. Bad packets generated from software bugs, corrupt DNS data, and local packets that escaped can create a significantly high false alarm rate [2].

Too few attacks- It is not uncommon for the number of real attacks to be far below the false alarm rate. Real attacks are often so far below the false alarm rate that they are often missed and ignored [2].

Signature updates-Many attacks are geared for specific versions of software that are usually outdated. A constantly changing library of signatures is needed to mitigate threats. Outdated signature databases can leave the IDS vulnerable to new strategies [2].

[edit] IDS evasion techniques

Intrusion detection system evasion techniques bypass detection by creating different states on the IDS and on the targeted computer. The adversary accomplishes this by manipulating either the attack itself or the network traffic that contains the attack.

[edit] Development

A preliminary concept of an IDS began with James P. Anderson and reviews of audit trails.[3] An example of an audit trail would be a log of user access.

Fred Cohen noted in 1984 (see Intrusion Detection) that it is impossible to detect an intrusion in every case and that the resources needed to detect intrusions grows with the amount of usage.

Dorothy E. Denning, assisted by Peter Neuman, published a model of an IDS in 1986 that formed the basis for many systems today.[4] Her model used statistics for anomaly detection, and resulted in an early IDS at SRI named the Intrusion detection expert system (IDES), which ran on Sun Workstations and could consider both user and network level data.[5] IDES had a dual approach with a rule-based Expert System to detect known types of intrusions plus a statistical anomaly detection component based on profiles of users, host systems, and target systems. Lunt proposed adding an Artificial neural network as a third component. She said all three components could then report to a resolver. SRI followed IDES in 1993 with the Next-generation Intrusion Detection Expert System (NIDES).[6]

The Multics intrusion detection and alerting system (MIDAS), an expert system using P-BEST and LISP, was developed in 1988 based on the work of Denning and Neuman.[7] Haystack was also developed this year using statistics to reduce audit trails.[8]

Wisdom & sense (W&S) was a statistics-based anomaly detector developed in 1989 at the Los Alamos National Laboratory.[9] W&S created rules based on statistical analysis, and then used those rules for anomaly detection.

In 1990, the Time-based inductive machine (TIM) did anomaly detection using inductive learning of sequential user patterns in Common LISP on a VAX 3500 computer.[10] The Network Security Monitor (NSM) performed masking on access matrices for anomaly detection on a Sun-3/50 workstation.[11] The Information Security Officer's Assistant (ISOA) was a 1990 prototype that considered a variety of strategies including statistics, a profile checker, and an expert system.[12] ComputerWatch at AT&T Bell Labs used statistics and rules for audit data reduction and intrusion detection.[13]

Then, in 1991, researchers at the University of California created a prototype Distributed intrusion detection system (DIDS), which was also an expert system.[14] The Network anomaly detection and intrusion reporter (NADIR), also in 1991, was a prototype IDS developed at the Los Alamos National Laboratory's Integrated Computing Network (ICN), and was heavily influenced by the work of Denning and Lunt.[15] NADIR used a statistics-based anomaly detector and an expert system.

The Lawrence Berkeley National Laboratory announced Bro in 1998 which used its own rule language for packet analysis from libpcap data.[16] Network Flight Recorder (NFR)in 1999 also used libpcap.[17] APE was developed as a packet sniffer, also using libpcap, in November, 1998, and was renamed Snort one month later.[18]

The Audit data analysis and mining (ADAM) IDS in 2001 used tcpdump to build profiles of rules for classifications.[19]

[edit] See also

[edit] Free Intrusion Detection Systems

[edit] Commercial Intrusion Detection Systems

[edit] References

  1. ^ a b c d e f g h i j k l Whitman, Michael, and Herbert Mattord. Principles of Information Secuirty. Canada: Thomson, 2009. Pages 290 & 301
  2. ^ a b c Anderson, Ross. Security Engineering. New York: Wiley, 2001. Pages 387-388
  3. ^ Anderson, James P., "Computer Security Threat Monitoring and Surveillance," Washing, PA, James P. Anderson Co., 1980.
  4. ^ Denning, Dorothy E., "An Intrusion Detection Model," Proceedings of the Seventh IEEE Symposium on Security and Privacy, May 1986, pages 119-131
  5. ^ Lunt, Teresa F., "IDES: An Intelligent System for Detecting Intruders," Proceedings of the Symposium on Computer Security; Threats, and Countermeasures; Rome, Italy, November 22-23, 1990, pages 110-121.
  6. ^ Lunt, Teresa F., "Detecting Intruders in Computer Systems," 1993 Conference on Auditing and Computer Technology, SRI International
  7. ^ Sebring, Michael M., and Whitehurst, R. Alan., "Expert Systems in Intrusion Detection: A Case Study," The 11th National Computer Security Conference, October, 1988
  8. ^ Smaha, Stephen E., "Haystack: An Intrusion Detection System," The Fourth Aerospace Computer Security Applications Conference, Orlando, FL, December, 1988
  9. ^ Vaccaro, H.S., and Liepins, G.E., "Detection of Anomalous Computer Session Activity," The 1989 IEEE Symposium on Security and Privacy, May, 1989
  10. ^ Teng, Henry S., Chen, Kaihu, and Lu, Stephen C-Y, "Adaptive Real-time Anomaly Detection Using Inductively Generated Sequential Patterns," 1990 IEEE Symposium on Security and Privacy
  11. ^ Heberlein, L. Todd, Dias, Gihan V., Levitt, Karl N., Mukherjee, Biswanath, Wood, Jeff, and Wolber, David, "A Network Security Monitor," 1990 Symposium on Research in Security and Privacy, Oakland, CA, pages 296-304
  12. ^ Winkeler, J.R., "A UNIX Prototype for Intrusion and Anomaly Detection in Secure Networks," The Thirteenth National Computer Security Conference, Washington, DC., pages 115-124, 1990
  13. ^ Dowell, Cheri, and Ramstedt, Paul, "The ComputerWatch Data Reduction Tool," Proceedings of the 13th National Computer Security Conference, Washington, D.C., 1990
  14. ^ Snapp, Steven R, Brentano, James, Dias, Gihan V., Goan, Terrance L., Heberlein, L. Todd, Ho, Che-Lin, Levitt, Karl N., Mukherjee, Biswanath, Smaha, Stephen E., Grance, Tim, Teal, Daniel M. and Mansur, Doug, "DIDS (Distributed Intrusion Detection System) -- Motivation, Architecture, and An Early Prototype," The 14th National Computer Security Conference, October, 1991, pages 167-176.
  15. ^ Jackson, Kathleen, DuBois, David H., and Stallings, Cathy A., "A Phased Approach to Network Intrusion Detection," 14th National Computing Security Conference, 1991
  16. ^ Paxson, Vern, "Bro: A System for Detecting Network Intruders in Real-Time," Proceedings of The 7th USENIX Security Symposium, San Antonio, TX, 1998
  17. ^ Amoroso, Edward, "Intrusion Detection: An Introduction to Internet Surveillance, Correlation, Trace Back, Traps, and Response," Intrusion.Net Books, Sparta, New Jersey, 1999, ISBN 0-9666700-7-8
  18. ^ Kohlenberg, Toby (Ed.), Alder, Raven, Carter, Dr. Everett F. (Skip), Jr., Foster, James C., Jonkman Marty, Raffael, and Poor, Mike, "Snort IDS and IPS Toolkit," Syngress, 2007, ISBN 978-1-59749-099-3
  19. ^ Barbara, Daniel, Couto, Julia, Jajodia, Sushil, Popyack, Leonard, and Wu, Ningning, "ADAM: Detecting Intrusions by Data Mining," Proceedings of the IEEE Workshop on Information Assurance and Security, West Point, NY, June 5-6, 2001

[edit] External links

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