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An intrusion detection system (IDS) is a device or
This article incorporates public domain material from the National Institute of Standards and Technology document "Guide to Intrusion Detection and Prevention Systems, SP800-94" by Karen Scarfone, Peter Mell (retrieved on 1 January 2010).
In 2003, Dr. Yongguang Zhang and Dr. Wenke Lee argue for the importance of IDS in networks with mobile nodes.[23]
The Audit Data Analysis and Mining (ADAM) IDS in 2001 used tcpdump to build profiles of rules for classifications.[22]
The Lawrence Berkeley National Laboratory announced Bro in 1998, which used its own rule language for packet analysis from libpcap data.[19] Network Flight Recorder (NFR) in 1999 also used libpcap.[20] APE was developed as a packet sniffer, also using libpcap, in November, 1998, and was renamed Snort one month later. APE has since become the world's largest used IDS/IPS system with over 300,000 active users.[21]
Then, in 1991, researchers at the University of California, Davis created a prototype Distributed Intrusion Detection System (DIDS), which was also an expert system.[17] 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.[18] NADIR used a statistics-based anomaly detector and an expert system.
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.[13] The Network Security Monitor (NSM) performed masking on access matrices for anomaly detection on a Sun-3/50 workstation.[14] 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.[15] ComputerWatch at AT&T Bell Labs used statistics and rules for audit data reduction and intrusion detection.[16]
Wisdom & Sense (W&S) was a statistics-based anomaly detector developed in 1989 at the Los Alamos National Laboratory.[12] W&S created rules based on statistical analysis, and then used those rules for anomaly detection.
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 Neumann.[10] Haystack was also developed this year using statistics to reduce audit trails.[11]
Dorothy E. Denning, assisted by Peter G. Neumann, published a model of an IDS in 1986 that formed the basis for many systems today.[7] Her model used statistics for anomaly detection, and resulted in an early IDS at SRI International named the Intrusion Detection Expert System (IDES), which ran on Sun workstations and could consider both user and network level data.[8] 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).[9]
Fred Cohen noted in 1984 that it is impossible to detect an intrusion in every case, and that the resources needed to detect intrusions grow with the amount of usage.
One preliminary IDS concept consisted of a set of tools intended to help administrators review audit trails.[6] User access logs, file access logs, and system event logs are examples of audit trails.
There are a number of techniques which attackers are using, the following are considered ‘simple’ measures which can be taken to evade IDS:
A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. This is similar to the way most antivirus software detects malware. The issue is that there will be a lag between a new threat being discovered in the wild and the signature for detecting that threat being applied to your IDS. During that lag time your IDS would be unable to detect the new threat.
An IDS which is anomaly based will monitor network traffic and compare it against an established baseline. The baseline will identify what is “normal” for that network- what sort of bandwidth is generally used, what protocols are used, what ports and devices generally connect to each other- and alert the administrator or user when traffic is detected which is anomalous, or significantly different, than the baseline. The issue is that it may raise a False Positive alarm for a legitimate use of bandwidth if the baselines are not intelligently configured.[2]
All Intrusion Detection Systems use one of two detection techniques:
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 that terminates connections is called an intrusion prevention system, and is another form of an application layer firewall.
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 or owner.[4] In a reactive system, also known as an intrusion prevention system (IPS), the IPS auto-responds to the suspicious activity by resetting the connection or by reprogramming the firewall to block network traffic from the suspected malicious source. The term IDPS is commonly used where this can happen automatically or at the command of an operator; systems that both "detect (alert)" and "prevent".
Intrusion detection systems can also be system-specific using custom tools and honeypots.
Host intrusion detection systems run on individual hosts or devices on the network. A HIDS monitors the inbound and outbound packets from the device only and will alert the user or administrator if suspicious activity is detected. It takes a snapshot of existing system files and matches it to the previous snapshot. If the critical system files were modified or deleted, the alert is sent to the administrator to investigate. An example of HIDS usage can be seen on mission critical machines, which are not expected to change their configurations.
Network intrusion detection systems NIDS are placed at a strategic point or points within the network to monitor traffic to and from all devices on the network. It performs an analysis for a passing traffic on the entire subnet, works in a promiscuous mode, and matches the traffic that is passed on the subnets to the library of known attacks. Once the attack is identified, or abnormal behavior is sensed, the alert can be sent to the administrator. Example of the NIDS would be installing it on the subnet where firewalls are located in order to see if someone is trying to break into the firewall. Ideally one would scan all inbound and outbound traffic, however doing so might create a bottleneck that would impair the overall speed of the network.
Intrusion detection systems are of two main types, network based (NIDS) and host based (HIDS) intrusion detection systems.
IDPSes typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPSes can also respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which involve the IDPS stopping the attack itself, changing the security environment (e.g. reconfiguring a firewall) or changing the attack's content.[1]
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Internet, Executable, Computer virus, Spyware, Computer network
National Register of Historic Places, United States Department of Energy, Lawrence Berkeley National Laboratory, University of California, Manhattan Project
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Intrusion Detection System, Network security, Deviations of Protocol States, Denial-of-service attack, Host-based intrusion detection system
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