Network Intrusion Detection Systems Types

NIDSs are normally classified according to the methods they use for attack detection; either as signature-based, or anomaly detection. Note, though, that almost all current NIDSs use a mixture of these approaches. Signature-based approaches, as mentioned earlier in this chapter, rely on some type of pattern matching. NIDS sensors parse the entire IP packet, and make decisions by means of a simple rule-based logic that is based upon signatures or regular expression matching. In other words, they compare the data within a packet payload to a database of predefined attack signatures (a string of bytes). Additionally, statistical or historical algorithms may supplement static pattern matching. Attack signatures usually consist of one or more of the following fields:

• Source and destination IP addresses, or an address or range
• TCP/UDP source and destination ports and ICMP type/code
• IP header flags and options
• TCP header flags and options
• A definition of the payload data to search (hex or ASCII)
• A starting point for the payload search (offset) and the search depth

Analysis of packet headers can be done economically since the locations of packet header fields are restricted by protocol standards. However, the payload contents are, for the most part, unconstrained. Therefore, searching through the payload for multiple string patterns within the datastream can be a computationally expensive task. The requirement that these searches be performed at wirespeed only adds to the cost.

Anomaly detection NIDSs are based on the assumption that normal traffic can be defined, and that attack or misuse patterns will differ from “normal” traffic. Heuristic-based signatures, on the other hand, use some type of algorithmic logic on which to determine their alarm decisions.

Note

Heuristic is the art and science of discovery and invention. The word comes from the same Greek root as “eureka,” which means “I find.” Heuristics defines a problem-solving technique in which the most appropriate solution is selected at successive stages of a program for use in the next step of the program. Heuristic approaches utilize simplification or an educated guess to reduce or limit the search for solutions. A heuristic can be a single algorithmic solution to a problem, but unlike an algorithm, heuristics does not guarantee optimal, or even feasible, solutions.

These algorithms are often statistical evaluations of the type of traffic being inspected. An example of a heuristic signature is a signature used to detect a port scan. This signature defines a particular threshold number of external probes against unique ports or a specific combination of ports. The signature may be further restricted by specification of the types of packets (for instance, SYN only) it reacts to. Interesting trends can be learned from these data, and it is possible to detect ongoing attacks based on these algorithms; however, the information that these systems provide is generally very nonspecific and requires extensive human investigation before actionable intelligence is gathered.

By creating baselines of normal behavior, anomaly-based NIDSs are able to detect when current network behavior deviates statistically from the norm. This capability theoretically gives an anomaly-based NIDS the capacity to detect new attacks that are either unknown or to detect attacks for which no signatures exist.

The major problem with this type of approach is that normal network traffic is difficult or impossible to define. Since normal network behavior can change easily and readily,anomaly-based NIDSs are prone to false positives. Additionally, inconsistency of detector performance, training issues (for example, how often an anomaly-based detection system should be retrained to ensure acceptable performance), and inadvertent incorporation of intrusive behavior into an NIDS concept of normal behavior during training negatively affect performance.