A Review of Intrusion Detection

In retrospect, you have learned about host-based intrusion detection systems (HIDS) and network-based intrusion detection systems (NIDS). Read this article on intrusion detection systems and note the strengths of HIDS and NIDS, and the overall pros and cons of intrusion detection systems.

5. THE FUTURE OF INTRUSION DETECTION

Intrusion detection fits in with a layered defense approach and intrusion detection technology is still growing and improving. Two things are certain intrusion detection is still a long way from being mature. Massive changes are in store for both areas. Some of the areas within intrusion detection, in which substantial and beneficial progress is likely to occur. These areas include the following:

    1. The continued reduction in reliance on signatures in intrusion detection
    2. The growth of intrusion prevention
    3. Advances in data correlation and alert correlation methods
    4. Advances in source determination
    5. Inclusion of integrated forensics functionality in IDSs.
    6. Greater use of honeypots.


  1. LOWER RELIANCE ON SIGNATURE- BASED INTRUSION DETECTION

The signature approach to intrusion detection, which traces back to the early 1990s, represents a major advance over the previous statistical-based approaches of the 1980s. Signatures are not only a relatively straightforward and intuitive approach to intrusion detection, but they are also efficient-often a set of only a few hundred signatures can result in reasonably high detection rates. Signature-based IDSs have proven popular and useful, so much so that you can count of some of these tools being available for a long time. Signature-based intrusion detection is beset with numerous limitations, however, including the following:

Because attacks have to occur before their signatures can be identified, signatures cannot be used in discovering new attacks. The white hat community is thus always one step behind the black hat community when it comes to new attack signatures. Many signatures in IDSs are badly outdated. One can always weed out obsolete signatures, but doing so requires a reasonable amount of unnecessary effort; good IDS vendors do not include such signatures in their products' signature sets in the first place.

Some attacks do not have single distinguishing signatures, but rather a wide range of possible variations. Each variation could conceivably be incorporated into a signature set, but doing so inflates the number of signatures, potentially hurting IDS performance. Additionally, keeping up with each possible variation is for all practical purposes an impossible task.

Signatures are almost useless in network-based IDSs when network traffic is encrypted.

The black hat community is becoming increasingly better at evading signature-based IDSs.

  1. INTRUSION PREVENTION

Intrusion prevention is another area that will grow dramatically in the future. Intrusion prevention is in its infancy. Anyone who thinks that IPSs and IDSs are diametrically opposed or that IPSs will eventually supplant IDSs is badly mistaken, however. An IDS is like a burglar alarm, something that provides information about past and ongoing activity that facilitates risk and threat assessment as well as investigations of suspicious and possibly wrongful activity. IPSs are designed to be defensive measures that stop or at least limit the negative consequences of attacks on systems and networks, not to yield the wealth of information that IDSs typically deliver.

One of the major, new offshoots of the last permutation of intrusion prevention discussed here is called active defense. Active defense means analyzing the condition of systems and networks and doing what is appropriate to deal with whatever is wrong. According to Dave Dittrich of the University of Washington, there are four levels of active defense:

      1. Local data collection, analysis, and blocking
      2. Remote collection of external data
      3. Remote collection of internal data
      4. Remote data alteration, attack suppression, and interdiction

One of the most important (and controversial) facets of the active defense approach to intrusion prevention is determining the appropriate response. The notion of appropriate response includes a consideration called proportionality of response, which ensures that the response is proportional to the threat. In the case of a hot that is flooding a network with fragmented packets, blocking traffic sent from that host is almost certainly the most appropriate response. If several dozen hosts known to be operated by an ISP repeatedly attack an organization's network, blocking all the traffic from the range of IP addresses owned by that ISP might be the most appropriate response. Some advocates of the active defense approach even believe that if a remote host is repeatedly attacking an organization's network, counterattacking that host, perhaps by flooding it with fragmented packets, thereby causing it to crash is the appropriate course of action. Although intrusion prevention appears promising, (as mentioned) it is very much in its infancy. Attack stave-off rates for intrusion prevention systems are nowhere as high as they need to pose a major deterrent to attacks. Additionally, false alarms can easily cause what effectively amounts to DoS within individual systems.

Intrusion prevention systems of the future are likely to be able to prevent a wider range of attacks, not only at the level of the individual host but also within organizations' networks and possibly even within the Internet itself. The last possibility is particularly intriguing. Perhaps some organizations such as the U.S. government's federal incident response team, FedCIRT, will continuously monitor all traffic bound for U.S. government sites and stop selectively malicious packets long before they reach the gateways of the government sites for which they are destined.

  1. DATA AND ALERT CORRELATION

Data correlation is becoming increasingly important. IDSs, firewalls, personal firewalls, and TCP wrappers are each capable of generating large amounts of data; collectively, they are capable of overwhelming intrusion detection analysts with data. Data aggregation helps ensure that data are available in a single location; data correlation enables analysts to recognize patterns in these data. Although current data correlation methods are for the most part not very sophisticated, future data correlation is likely to become much better. How will data correlation algorithms need to change? Waltz and Llinas (in Multisensor Data Fusion, Boston: Artech House, 1990) have developed criteria for systems designed to fuse data must be able to, saying that these systems must be able to do the following:

      • Distinguish parameters of interest from noise.
      • Distinguish among different objects in space and time
      • Adequately track and capture each desired type of event and data
      • Sample the data and events of interest with sufficient frequency
      • Provide accurate measurements
      • Ensure that each variable that is measured adequately represents the desired
      • Types of categories.
      • Provide access to both raw and correlated data
      • Preserve the temporal characteristics of data and events

It is unlikely that all systems designed to fuse data will meet every one of these requirements. The more of these requirements that a system meets, however, the more useful in data fusion/correlation it is likely to be. Currently, one of the greatest barriers to automated data fusion has been the lack of a common format for data from intrusion detection systems. Although common formats have been proposed, little agreement has resulted. Agreement upon a single data format would thus constitute a giant step forward.

  1. SOURCE DETERMINATION

Source determination means determining the origin of network traffic. Given how easy it is to spoof IP addresses, any source IP address in conventional IP packets must be viewed with suspicion. Tools that fabricate packets, inserting any desired IP address into the IP headers, are freely available on the Internet. Many countermeasures, most notably strong authentication methods (such as the use of Smart Cards) and digital signatures, can remove doubt concerning the identity of individuals who initiate transactions, but they are not designed to identify the source IP addresses from which transactions originate. IPsec, the secure IP protocol, effectively removes any doubt concerning the validity of IP source addresses, but IPsec has, unfortunately, not grown in popularity in proportion to its many merits.

    1. INTEGRATED FORENSICS CAPABILITIES
      Forensics means using special procedures that preserve evidence for legal purposes. When people think of forensics, they normally envision investigators archiving the contents of hard drives to a machine that runs forensics software, making hard copies of audit logs, and labeling and bagging peripherals such as keyboards and mice. Many people fail to realize that IDSs are potentially one of the best sources of forensics data, especially if the IDSs capture and store keystrokes. A few IDS vendors are starting to build forensics capabilities into their products, capabilities that enable those who use the systems to make copies of IDS output, create a hash value of the output (to ensure its integrity), search it for special keywords or graphic content, and so on.

    2. USE OF HONEYPOTS IN INTRUSION DETECTION
      A honeypot is a decoy server that looks and acts like a normal server, but that does not run or support normal server functions. The main purpose of deploying honeypots is to observe the behavior of attackers in a safe environment, one in which there is (at least in theory) no threat to normal, operational systems. Having proven especially useful as a reconnaissance tool that yields information concerning what kinds of attacks is occurring and how often, honeypots have gained a great deal of acceptance within the information security arena.