Other Free Encyclopedias » Online Encyclopedia » Encyclopedia - Featured Articles » Contributed Topics from F-J


attacks based network attack

H. Gunes Kayacik
Dalhousie University, Canada

A. Nur Zincir-Heywood
Dalhousie University, Canada

Malcolm I. Heywood
Dalhousie University, Canada


Along with its numerous benefits, the Internet also created numerous ways to compromise the security and stability of the systems connected to it. In 2003, 137529 incidents were reported to CERT/CC© while in 1999, there were 9859 reported incidents (CERT/CC©, 2003). Operations, which are primarily designed to protect the availability, confidentiality, and integrity of critical network information systems, are considered to be within the scope of security management. Security management operations protect computer networks against denial-of-service attacks, unauthorized disclosure of information, and the modification or destruction of data. Moreover, the automated detection and immediate reporting of these events are required in order to provide the basis for a timely response to attacks (Bass, 2000). Security management plays an important, albeit often neglected, role in network management tasks.

Defensive operations can be categorized in two groups: static and dynamic. Static defense mechanisms are analogous to the fences around the premises of a building. In other words, static defensive operations are intended to provide barriers to attacks. Keeping operating systems and other software up-to-date and deploying firewalls at entry points are examples of static defense solutions. Frequent software updates can remove software vulnerabilities that are susceptible to exploits. By providing access control at entry points, they therefore function in much the same way as a physical gate on a house. In other words, the objective of a firewall is to keep intruders out rather than catching them. Static defense mechanisms are the first line of defense, they are relatively easy to deploy and naturally provide significant defense improvement compared to the initial unguarded state of the computer network. Moreover they act as the foundation for more sophisticated defense mechanisms.

No system is totally foolproof. It is safe to assume that intruders are always one step ahead in finding security holes in current systems. This calls attention to the need for dynamic defenses. Dynamic defense mechanisms are analogous to burglar alarms, which monitor the premises to find evidence of break-ins. Built upon static defense mechanisms, dynamic defense operations aim to catch the attacks and log information about the incidents such as source and nature of the attack. Therefore, dynamic defense operations accompany the static defense operations to provide comprehensive information about the state of the computer networks and connected systems.

Intrusion detection systems are examples of dynamic defense mechanisms. An intrusion detection system (IDS) is a combination of software and hardware, which collects and analyzes data collected from networks and the connected systems to determine if there is an attack (Allen, Christie, Fithen, McHugh, Pickel, & Stoner, 1999). Intrusion detection systems complement static defense mechanisms by double-checking firewall configuration, and then attempt to catch attacks that firewalls let in or never perceive (such as insider attacks). IDSs are generally analyzed from two aspects:

  • IDS Deployment: Whether to monitor incoming traffic or host information.
  • Detection Methodologies: Whether to employ the signatures of known attacks or to employ the models of normal behavior.

Regardless of the aspects above, intrusion detection systems correspond to today’s dynamic defense mechanisms. Although they are not flawless, current intrusion detection systems are an essential part of the formulation of an entire defense policy.


Different detection methodologies can be employed to search for the evidence of attacks. Two major categories exist as detection methodologies: misuse and anomaly detection. Misuse detection systems rely on the definitions of misuse patterns i.e., the descriptions of attacks or unauthorized actions (Kemmerer & Vigna, 2002). A misuse pattern should summarize the distinctive features of an attack and is often called the signature of the attack in question. In the case of signature based IDS, when a signature appears on the resource monitored, the IDS records the relevant information about the incident in a log file. Signature based systems are the most common examples of misuse detection systems. In terms of advantages, signature based systems, by definition, are very accurate at detecting known attacks, where these are detailed in their signature database. Moreover, since signatures are associated with specific misuse behavior, it is easy to determine the attack type. On the other hand, their detection capabilities are limited to those within signature database. As new attacks are discovered, a signature database requires continuous updating to include the new attack signatures, resulting in potential scalability problems.

As opposed to misuse IDSs, anomaly detection systems utilize models of the acceptable behavior of the users. These models are also referred to as normal behavior models. Anomaly based IDSs search for any deviation from the (characterized) normal behavior. Deviations from the normal behavior are considered as anomalies or attacks. As an advantage over signature based systems, anomaly based systems can detect known and unknown (i.e., new) attacks as long as the attack behavior deviates sufficiently from the normal behavior. However, if the attack is similar to the normal behavior, it may not be detected. Moreover, it is difficult to associate deviations with specific attacks since the anomaly based IDSs only utilize models of normal behavior. As the users change their behavior as a result of additional service or hardware, even the normal activities of a user may start raising alarms. In that case, models of normal behavior require be redefinition in order to maintain the effectiveness of the anomaly based IDS.

Human input is essential to maintain the accuracy of the system. In the case of signature based systems, as new attacks are discovered, security experts examine the attacks to create corresponding detection signatures. In the case of anomaly systems, experts are needed to define the normal behavior. Therefore, regardless of the detection methodology, frequent maintenance is essential to uphold the performance of the IDS.

Given the importance of IDSs, It is imperative to test them to determine their performance and eliminate their weaknesses. For this purpose, researchers conduct tests on standard benchmarks (Kayacik, Zincir, & Heywood, 2003; Pickering, 2002). When measuring the performance of intrusion detection systems, the detection and false positive rates are used to summarize different characteristics of classification accuracy. In simple terms, false positives (or false alarms) are the alarms generated by a non-existent attack. For instance, if an IDS raises alarms for the legitimate activity of a user, these log entries are false alarms. On the other hand, detection rate is the number of correctly identified attacks over all attack instances, where correct identification implies the attack is detected by its distinctive features. An intrusion detection system becomes more accurate as it detects more attacks and raises fewer false alarms. A receiver operating characteristic or ROC, where this details how system performance varies as a function of different parameters, typically characterizes the sensitivity of the IDS.


In addition to the detection methodologies, data is collected from two main sources: traffic passing through the network and the hosts connected to the network. Therefore, according to where they are deployed, IDSs are divided into two categories, those that analyze network traffic and those that analyze information available on hosts such as operating system audit trails. The current trend in intrusion detection is to combine both host based and network based information to develop hybrid systems and therefore not rely on any one methodology. In both approaches however, the amount of audit data is extensive, thus incurring large processing overheads. A balance therefore exists between the use of resources, and the accuracy and timeliness of intrusion detection information.

Network based IDS inspect the packets passing through the network for signs of an attack. However, the amount of data passing through the network stream is extensive, resulting in a trade off between the number of detectors and the amount of analysis each detector performs. Depending on throughput requirements, a network based IDS may inspect only packet headers or include the content. Moreover, multiple detectors are typically employed at strategic locations in order to distribute the task. Conversely, when deploying attacks, intruders can evade IDSs by altering the traffic. For instance, fragmenting the content into smaller packets causes IDSs to see one piece of the attack data at a time, which is insufficient to detect the attack. Thus, network based IDSs, which perform content inspection, need to assemble the received packets and maintain state information of the open connections, where this becomes increasingly difficult if a detector only receives part of the original attack or becomes “flooded” with packets.

A host-based IDS monitors resources such as system logs, file systems, processor, and disk resources. Example signs of intrusion on host resources are critical file modifications, segmentation fault errors, crashed services, or extensive usage of the processors. As opposed to network-based IDSs, host-based IDSs can detect attacks that are transmitted over an encrypted channel. Moreover, information regarding the software that is running on the host is available to host-based IDS. For instance, an attack targeting an exploit on an older version of a Web server might be harmless for the recent versions. Network-based IDSs have no way of determining whether the exploit has a success chance, or of using a priori information to constrain the database of potential attacks. Moreover, network management practices are often critical in simplifying the IDS problem by providing appropriate behavioral constraints, thus making it significantly more difficult to hide malicious behaviors (Cunningham, Lippmann, & Webster, 2001).


The intrusion detection problem has three basic competing requirements: speed, accuracy, and adaptability. The speed problem represents a quality of service issue. The more analysis (accurate) the detector, the higher the computational overhead. Conversely, accuracy requires sufficient time and information to provide a useful detector. Moreover, the rapid introduction of both new exploits and the corresponding rate of propagation require that detectors be based on a very flexible/scalable architecture. In today’s network technology where gigabit Ethernet is widely available, existing systems face significant challenges merely to maintain pace with current data streams (Kemmerer & Vigna, 2002).

An intrusion detection system becomes more accurate as it detects more attacks and raises fewer false alarms. IDSs that monitor highly active resources are likely to have large logs, which in turn complicate the analysis. If such an IDS has a high false alarm rate, the administrator will have to sift through thousands of log entries, which actually represent normal events, to find the attack-related entries. Therefore, increasing false alarm rates will decrease the administrator’s confidence in the IDS. Moreover, intrusion detection systems are still reliant on human input in order to maintain the accuracy of the system. In case of signature based systems, as new attacks are discovered, security experts examine the attacks to create corresponding detection signatures. In the case of anomaly systems, experts are needed to define the normal behavior. This leads to the adaptability problem. The capability of the current intrusion detection systems for adaptation is very limited. This makes them inefficient in detecting new or unknown attacks or adapting to changing environments (i.e., human intervention is always required). Although a new research area, incorporation of machine learning algorithms provides a potential solution for accuracy and adaptability of the intrusion detection problem.


Intrusion detection systems reviewed here are by no means a complete list but a subset of open source and commercial products, which are intended to provide readers different intrusion detection practices.

  • Snort: Snort is one of the best-known lightweight IDSs, which focuses on performance, flexibility, and simplicity. It is an open-source intrusion detection system that is now in quite widespread use (Roesch, 1999). Snort is a network based IDS which employs signature based detection methods. It can detect various attacks and Probes including instances of buffer overflows, stealth port scans, common gateway interface attacks, and service message block system Probes (Roesch, 1999). Hence, Snort is an example of active intrusion detection systems that detects possible attacks or access violations while they are occurring (CERT/CC ©, 2001).
  • Cisco IOS (IDS Component): Cisco IOS provides a cost effective way to deploy a firewall with network based intrusion detection capabilities. In addition to the firewall features, Cisco IOS Firewall has 59 built-in, static signatures to detect common attacks and misuse attempts (Cisco Systems, 2003). The IDS process on the firewall router inspects packet headers for intrusion detection by using those 59 signatures. In some cases routers may examine the whole packet and maintain the state information for the connection. Upon attack detection, the firewall can be configured to log the incident, drop the packet, or reset the connection.
  • Tripwire: When an attack takes place, attackers usually replace critical system files with their versions to inflict damage. Tripwire (Tripwire Web Site, 2004) is an open-source host-based tool, which performs periodic checks to determine which files are modified in the file system. To do so, Tripwire takes snapshots of critical files. Snapshot is a unique mathematical signature of the file where even the smallest change results in a different snapshot. If the file is modified, the new snapshot will be different than the old one; therefore critical file modification would be detected. Tripwire is different from the other intrusion detection systems because rather than looking for signs of intrusion, Tripwire looks for file modifications.


As indicated above, various machine learning approaches have been proposed in an attempt to improve on the generic signature-based IDS. The basic motivation is to measure how close a behavior is to some previously established gold standard of misuse or normal behavior. Depending on the level of a priori or domain knowledge, it may be possible to design detectors for specific categories of attack (e.g., denial of service, user to root, remote to local). Generic machine learning approaches include clustering or data mining in which case the data is effectively unlabeled. The overriding assumption is that behaviors are sufficiently different for normal and abnormal behaviors to fall into different “clusters”. Specific examples of such algorithms include artificial immune systems (Hofmeyr & Forrest, 2000) as well as various neural network (Kayacik, Zincir-Heywood, & Heywood, 2003; Lee & Heinbuch, 2001) and clustering algorithms (Eskin, Arnold, Prerau, Portnoy, & Stolfo, 2002).

Naturally the usefulness of machine learning systems is influenced by the features on which the approach is based (Lee & Stolfo, 2001). Domain knowledge that has the capability to significantly simplify detectors utilizing machine learning often make use of the fact that attacks are specific to protocol-service combinations. Thus, first partitioning data based on the protocol-service combination significantly simplifies the task of the detector (Ramadas, Ostermann, & Tjaden, 2003).

When labeled data is available then supervised learning algorithms are more appropriate. Again any number of machine learning approaches have been proposed, including: decision trees (Elkan, 2000), neural networks (Hofmann & Sick, 2003) and genetic programming (Song, Heywood, & Zincir-Heywood, 2003). However, irrespective of the particular machine learning methodology, all such methods need to address the scalability problem. That is to say, datasets characterizing the IDS problem are exceptionally large (by machine learning standards). Moreover, the continuing evolution of the base of attacks also requires that any machine learning approach also have the capability for online or incremental learning. Finally, to be of use to network management practitioners it would also be useful if machine learning solutions were transparent. That is to say, rather than provide “black box solutions”, it is much more desirable if solutions could be reverse engineered for verification purposes. Many of these issues are still outstanding. For example cases that explicitly provide scalable solutions (Song, Heywood, & Zincir-Heywood, 2003) or automatically identify weaknesses in the IDS (Dozier, Brown, Cain, & Hurley, 2004) are only just appearing.


An intrusion detection system is a crucial part of the defensive operations that complements the static defenses such as firewalls. Essentially, intrusion detection systems search for signs of an attack and flag when an intrusion is detected. In some cases they may take an action to stop the attack by closing the connection or report the incident for further analysis by network administrators. According to the detection methodology, intrusion detection systems are typically categorized as misuse detection and anomaly detection systems. From a deployment perspective, they are be classified as network based or host based although such distinction is coming to an end in today’s intrusion detection systems where information is collected from both network and host resources. In terms of performance, an intrusion detection system becomes more accurate as it detects more attacks and raises fewer false alarms. Future advances in IDS are likely to continue to integrate more information from multiple sources (sensor fusion) whilst making further use of artificial intelligence to minimize the size of log files necessary to support signature databases. Human intervention, however, is certainly necessary and set to continue for the foreseeable future.

Investment Strategy for Integrating Wireless Technology Into Organizations - INTRODUCTION, MAJOR UNCERTAINTIES AND RISKS IN THE FIELD OF WIRELESS TECHNOLOGIES [next] [back] Introduction to the Biographies of Selected Innovators of Photographic Technology - Biographies of Selected Innovators of Photographic Technology from the 19th Century

User Comments

Your email address will be altered so spam harvesting bots can't read it easily.
Hide my email completely instead?

Cancel or

Vote down Vote up

over 6 years ago

a web intruction dection mechanism based on feature base data

Vote down Vote up

over 4 years ago

Great article!