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When IDSs were first contemplated in the 1970s and 1980s, many focused on anomaly detection Researchers continue to devote energy to this method, but pattern-based detection has been the dominant strategy for the last decade Pattern-based detection prevailed for a few simple reasons First, it was easier for developers to implement Searching a packet or reassembled stream for a string of characters is conceptually simple Ever-increasing traffic loads and deployment of encryption frustrate this approach, but pattern matching survives Second, it is easier for analysts to understand If an analyst knows the detection product is watching for a certain pattern, it makes sense when the IDS reports seeing that pattern If the pattern does not indicate a malicious event occurred, it's not the IDS's fault Verification has been lacking, but NSM approaches and integration of assessment techniques make pattern matching increasingly effective Third, it is much easier to deploy a rule based on a new pattern than it is to implement a new algorithm for anomaly-based intrusion detection Snort rules are released within hours of the discovery of a new worm, exploit, and other malicious code Engineers and analysts can plug the new rule into their product and validate or tune it as necessary Anomaly detection is not dead, however Pattern-matching systems are frequently compared to signature-based antivirus products If an antivirus product strictly relied on patterns to intercept and defeat malware, users would suffer infections much more often Antivirus products supplement their pattern-matching features with heuristic detection algorithms Heuristic techniques concentrate more on the acts the malware take, like opening network sockets, sending e-mail, and so on, and less on the code used to bring these acts to life Implementing heuristic detection or anomaly-based detection requires knowing what is normal, suspicious, and malicious We encountered those terms earlier in the book with respect to manual inspection of security events In some sense a human analyst is an anomaly detection device An experienced human analyst develops a sense of what is normal for the organizations he or she monitors Seeing an alert or session that falls outside those boundaries of normality prompts the analyst to investigate Validating these unusual events requires access to high-fidelity network traffic, as is provided by session data and full content data Network profiling is related to anomaly detection It's the idea that organizations should develop a formal sense of the devices and traffic patterns required for normal operational use Introduction of new devices or traffic types should raise an alarm Marcus Ranum calls this technique first seen anomaly detection: "[A first seen anomaly detection] system treats the first time it sees an instance of a thing as an anomaly It's accurate that something never seen before is always anomalous, in at least a few senses of the word When I was looking at statistical anomaly detection for IDS I realized that the first seen anomaly was 99% of what you'd get [with statistical anomaly detection products]"
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This quote is from a personal e-mail dated December 1, 2003
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Network profilers often turn to asset inventory products to keep track of active devices and applications The active vulnerability assessment community has moved into the asset inventory product space, simply to improve the quality of the results Products like Foundstone Enterprise report on new hosts and services to facilitate network management Intrusion detection products are beginning to collect contextual information on the network environment to improve attack validation All of these trends point to increased network awareness With this new information available, integrated anomaly detection products might have a chance of widespread deployment in the near future
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