Intrusion detection system


An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Any intrusion activity or violation is typically either reported to an administrator or collected centrally using a security information and event management system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms.
IDS types range in scope from single computers to large networks. The most common classifications are network intrusion detection systems and host-based intrusion detection systems. A system that monitors important operating system files is an example of an HIDS, while a system that analyzes incoming network traffic is an example of an NIDS. It is also possible to classify IDS by detection approach. The most well-known variants are signature-based detection and anomaly-based detection. Another common variant is reputation-based detection. Some IDS products have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. Intrusion detection systems can also serve specific purposes by augmenting them with custom tools, such as using a honeypot to attract and characterize malicious traffic.

Comparison with firewalls

Although they both relate to network security, an IDS differs from a firewall in that a conventional network firewall uses a static set of rules to permit or deny network connections. It implicitly prevents intrusions, assuming an appropriate set of rules have been defined. Essentially, firewalls limit access between networks to prevent intrusion and do not signal an attack from inside the network. An IDS describes 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 of common computer attacks, and taking action to alert operators. A system that terminates connections is called an intrusion prevention system, and performs access control like an application layer firewall.

Intrusion detection category

IDS can be classified by where detection takes place or the detection method that is employed.

Analyzed activity

Network intrusion detection systems

Network intrusion detection systems 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 of passing traffic on the entire subnet, and matches the traffic that is passed on the subnets to the library of known attacks. Once an attack is identified, or abnormal behavior is sensed, the alert can be sent to the administrator. NIDS function to safeguard every device and the entire network from unauthorized access.
An example of an 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. OPNET and NetSim are commonly used tools for simulating network intrusion detection systems. NID Systems are also capable of comparing signatures for similar packets to link and drop harmful detected packets which have a signature matching the records in the NIDS. When we classify the design of the NIDS according to the system interactivity property, there are two types: on-line and off-line NIDS, often referred to as inline and tap mode, respectively. On-line NIDS deals with the network in real time. It analyses the Ethernet packets and applies some rules, to decide if it is an attack or not. Off-line NIDS deals with stored data and passes it through some processes to decide if it is an attack or not.
NIDS can be also combined with other technologies to increase detection and prediction rates. Artificial Neural Network based IDS are capable of analyzing huge volumes of data due to the hidden layers and non-linear modeling, however this process requires time due its complex structure. This allows IDS to more efficiently recognize intrusion patterns. Neural networks assist IDS in predicting attacks by learning from mistakes; ANN based IDS help develop an early warning system, based on two layers. The first layer accepts single values, while the second layer takes the first's layers output as input; the cycle repeats and allows the system to automatically recognize new unforeseen patterns in the network. This system can average 99.9% detection and classification rate, based on research results of 24 network attacks, divided in four categories: DOS, Probe, Remote-to-Local, and user-to-root.

Host intrusion detection systems

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, an 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.

Detection method

Signature-based

Signature-based IDS is the detection of attacks by looking for specific patterns, such as byte sequences in network traffic, or known malicious instruction sequences used by malware. This terminology originates from anti-virus software, which refers to these detected patterns as signatures. Although signature-based IDS can easily detect known attacks, it is difficult to detect new attacks, for which no pattern is available.
In signature-based IDS, the signatures are released by a vendor for all its products. On-time updating of the IDS with the signature is a key aspect.

Anomaly-based

s were primarily introduced to detect unknown attacks, in part due to the rapid development of malware. The basic approach is to use machine learning to create a model of trustworthy activity, and then compare new behavior against this model. Since these models can be trained according to the applications and hardware configurations, machine learning based method has a better generalized property in comparison to traditional signature-based IDS. Although this approach enables the detection of previously unknown attacks, it may suffer from false positives: previously unknown legitimate activity may also be classified as malicious. Most of the existing IDSs suffer from the time-consuming during detection process that degrades the performance of IDSs. Efficient feature selection algorithm makes the classification process used in detection more reliable.
New types of what could be called anomaly-based intrusion detection systems are being viewed by Gartner as User and Entity Behavior Analytics and network traffic analysis. In particular, NTA deals with malicious insiders as well as targeted external attacks that have compromised a user machine or account. Gartner has noted that some organizations have opted for NTA over more traditional IDS.

Intrusion prevention

Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPS for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPS have become a necessary addition to the security infrastructure of nearly every organization.
IDPS typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPS 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 or changing the attack's content.
Intrusion prevention systems, also known as intrusion detection and prevention systems, are network security appliances that monitor network or system activities for malicious activity. The main functions of intrusion prevention systems are to identify malicious activity, log information about this activity, report it and attempt to block or stop it..
Intrusion prevention systems are considered extensions of intrusion detection systems because they both monitor network traffic and/or system activities for malicious activity. The main differences are, unlike intrusion detection systems, intrusion prevention systems are placed in-line and are able to actively prevent or block intrusions that are detected. IPS can take such actions as sending an alarm, dropping detected malicious packets, resetting a connection or blocking traffic from the offending IP address. An IPS also can correct errors, defragment packet streams, mitigate TCP sequencing issues, and clean up unwanted transport and network layer options.

Classification

Intrusion prevention systems can be classified into four different types:
  1. Network-based intrusion prevention system : monitors the entire network for suspicious traffic by analyzing protocol activity.
  2. Wireless intrusion prevention system : monitor a wireless network for suspicious traffic by analyzing wireless networking protocols.
  3. Network behavior analysis : examines network traffic to identify threats that generate unusual traffic flows, such as distributed denial of service attacks, certain forms of malware and policy violations.
  4. Host-based intrusion prevention system : an installed software package which monitors a single host for suspicious activity by analyzing events occurring within that host.