Intrusion Detection Systems (IDS) Part 2 – Classification; methods; techniques
This network security tool uses either of two main techniques described in more detail below. The first one, anomaly detection, explores issues in intrusion detection associated with deviations from normal system or user behavior. The second employs signature detection to discriminate between anomaly or attack patterns signatures and known intrusion detection signatures. Both methods have their distinct advantages and disadvantages as well as suitable application areas of intrusion detection. When considering the area being the source of data used for intrusion detection, another classification of intrusion detection systems can be used in terms of the type of the protected system.
Intrusion Detection Systems (IDS) Part 2 – Classification; methods; techniques
This network security tool uses either of two main techniques described in more detail below. The first one, anomaly detection, explores issues in intrusion detection associated with deviations from normal system or user behavior. The second employs signature detection to discriminate between anomaly or attack patterns signatures and known intrusion detection signatures. Both methods have their distinct advantages and disadvantages as well as suitable application areas of intrusion detection.
When considering the area being the source of data used for intrusion detection, another classification of intrusion detection systems can be used in terms of the type of the protected system. These examine host-based incoming and outgoing network connections. These are particularly related to the unauthorized connection attempts to TCP or UDP ports and can also detect incoming portscans.
Systems that examine network traffic packets that attempts to access the host. These systems protect the host by intercepting suspicious packets and looking for aberrant payloads packet inspection. Systems that monitor login activity onto the networking layer of their protected host HostSentry. Their role is to monitor log-in and log-out attempts, looking for unusual activity on a system occurring at unexpected times, particular network locations or detecting multiple login attempts particularly failed ones.
Systems that monitor actions of a super-user root who has the highest privileges LogCheck. IDS scans for unusual activity, increased super-user activity or actions performed at particular times, etc. Systems that monitor the system register state Windows platform only. They are designed to detect any illegal changes in the system register and alert the system administrator to this fact. Kernel based intrusion detection systems [Els00]. These systems examine the state of key operating system files and streams, preventing buffer overflow, blocking unusual interprocess communications, preventing an intruder from attacking the system.
In addition, they can block a part of the actions undertaken by the super-user restricting privileges. The HIDS reside on a particular computer and provide protection for a specific computer system.
They are not only equipped with system monitoring facilities but also include other modules of a typical IDS, for example the response module see Part I of the cycle. The NIDS reassemble and analyze all network packets that reach the network interface card operating in promiscuous mode.
They do not only deal with packets going to a specific host – since all the machines in a network segment benefit from the protection of the NIDS. Network-based IDS can also be installed on active network elements, for example on routers. Since intrusion detection for example flood-type attack employs statistical data on the network load, a certain type of dedicated NIDS can be separately distinguished, for example, those that monitor the traffic Novell Analyzer, Microsoft Network Monitor.
These capture all packets that they see on the network segment without analyzing them and just focusing on creating network traffic statistics. Typical network-based intrusion systems are: In fact, a NNIDS operates very much like a hybrid per-host NIDS since a single agent usually processes the network traffic directed to the host it runs upon an “every man for himself approach”.
The main reason for introducing such hybrid IDS was the need to work online with encrypted networks and their data destined to the single host only the source and destination can see decrypted network traffic. Most large commercially offered intrusion detection systems are shim-hybrid ones, i. The HIDS that look only at their host traffic can easily detect local-to-local attacks or local-to-root attacks, since they have a clear concept of locally available information, for example they can exploit user IDS.
Also, anomaly detection tools feature a better coverage of internal problems since their detection ability is based on the normal behavior patterns of the user.
The IDS can operate as standalone, centralized applications or integrated applications that create a distributed system. The latter have a particular architecture with autonomous agents that are able to take preemptive and reactive measures and even to move over the network. One may categorize intrusion detection systems in terms of behavior i.
They may also be active which means that they detect and respond to attacks, attempt to patch software holes before getting hacked or act proactively by logging out potential intruders, or blocking services. This is discussed in Part III of the cycle. Figure 1: Classification of intrusion detection systems Audit trail processing vs. Audit trail analysis is the prevalent method used by periodically operated systems.
In contrast, the IDS deployable in real-time environments are designed for online monitoring and analyzing system events and user actions.
Audit Trail Processing There are many issues related to audit trail event log processing. Storing audit trail reports in a single file must be avoided since intruders may use this feature to make unwanted changes. It is far better to keep a certain number of event log copies spread over the network, though it would imply adding some overheads to both the system and network.
Further, from the functionality point of view, recording every event possible means a noticeable consumption of system resources both the local system and network involved. Log compression, instead, would increase the system load. Specifying which events are to be audited is difficult because certain types of attacks may pass undetected.
It is also difficult to predict how large audit files can be – through experience one can only make a rough estimate. Also, an appropriate setting of a storage period for current audit files is not a straightforward task. In general, this depends on a specific IDS solution and its correlation engine.
Certainly, archive files should be stored as copies for retrieval analysis purposes. Log processing systems are vulnerable to Denial of Service DoS attacks that render audit mechanisms unreliable and unusable by overflowing the system’s free space.
The main reasons for having an audit function include: Repelling potential intruders by simply making them aware of the existence of the auditing means.
The audit reporting may provide a form of defense for an innocent user, for example possible involved in hacking attempts. The log event-based IDS method needs to have the following capabilities: Allowing of parameterization for easy recording of system event logs and user activities, Providing an option of self-disengagement of logging mechanisms in the event of insufficient space or DoS attacks; Audit trail processing using additional mechanisms aggregation, artificial intelligence, and data mining because of large file sizes, A reasonable minimum system resource consumption for auditing purposes.
Examples of intrusion detection systems that use audit trail processing are: With its built-in expert system, it analyzes all event logs to recognize abnormal user behavior.
ACID has a user query builder, which can analyze packets down to their payload, in order to find identical alerts among databases, which match certain criteria. It can also manage alerts and generate a variety of statistics. On-the fly processing With on the fly processing, an IDS performs online verification of system events. Generally, a stream of network packets is constantly monitored constantly.
With this type of processing, intrusion detection uses the knowledge of current activities over the network to sense possible attack attempts it does not look for successful attacks in the past. Given the computation complexity, the algorithms that are used here are limited to quick and efficient procedures that are often algorithmically simple.
This is due to a compromise between the main requisite – attack detection capability and the complexity of data processing mechanisms used in the detection itself. At the same time, construction of an on-the-fly processing IDS tool requires a large amount of RAM buffers since no data storage is used. Therefore, such an IDS may sometime miss packets, because realistic processing of too many packets is not available.
The amount of data collected by the detector is small since it views only buffer contents. Hence, only small portions of information can be analyzed for searching certain values or sequences. The main method used in real-time detecting is simply looking for character strings in the transport layer packets 3.
An example of packet pathology is when both the source and destination port addresses are set to This is not compliant with FTP specifications since the source port number must be greater than As a contrast with standard inspection methods, only selective packets in a data stream get inspected, and the inspection process only looks for “state” information, such as whether a packet contains malicious code.
Application-based IDS employ so-called standard packet inspection to analyze the TCP packet payload headers are excluded. With this method, only selective, correlated packets in a data stream get examined and the inspection process looks for information about whether a packet matches typical packets commands of a given protocol.
Thus, POP3 denial of service vulnerability is exploited by saturating POP3 server with multiple requests to execute a command. Here, the attack signature is developed by the number of commands sent by a given system and by establishing the alarm threshold.
The method assumes that anomalies found in packet inspection, checking of packet size and threshold values are manifestations of a denial of service attack, also at the transport layer, for example Ping of Death attack.
Another example of standard packet inspection IDSs include detecting email viruses before they get to email boxes by looking for matching email titles or attachment names. One may also search for malicious code which may compromise the system if it is attacked by, for example, buffer overflow exploits looking for signatures that monitor the user session status to disallow, for example, listing of directory structure on a FTP server before a successful user login [Dor02a.
A drawback of the high layer analysis approach lies in the fact that it is time-consuming and operating environment-dependent application layer protocols that vary from operating system to operating system. The real-time based IDSs offer the following advantages: Disadvantages include: Source identification is accomplished based on the network address derived from the packet not, for example, with using the network ID.
The source address may be spoofed, making attacks harder to trace and respond to automatically. That they cannot handle encrypted packets thereby not providing essential information required for intrusion detection. Since the analytical module uses a limited portion of source information buffer content only , its detection capability is limited. A continuous scanning of network traffic reduces the network throughout the segment on which the IDS sits. This is of particular importance when an IDS tool is deployed near the firewall.
Anomaly vs. However translating user behaviors or a complete user-system session in a consistent security-related decision is often not that simple – many behavior patterns are unpredictable and unclear Fig. In order to classify actions, intrusion detection systems take advantage of the anomaly detection approach, sometimes referred to as behavior based [Deb99] or attack signatures i. Figure 2: Behavior of the user in the system [Jon00] Normal behavior patterns – anomaly detection Normal behavior patterns are useful in predicting both user and system behavior.
Here, anomaly detectors construct profiles that represent normal usage and then use current behavior data to detect a possible mismatch between profiles and recognize possible attack attempts.
In order to match event profiles, the system is required to produce initial user profiles to train the system with regard to legitimate user behaviors. There is a problem associated with profiling: An inappropriate profile will be able to detect all possible intrusive activities.
Furthermore, there is an obvious need for profile updating and system training which is a difficult and time-consuming task.
Audit trail processing vs. on-the-fly processing
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Classification of intrusion detection systems
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