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While more detail can be extracted by many of the tools presented in this chapter, these six elements are he core of any session data collection strategy In the context of this chapter, we care about session data for its ability to track intruder activities in a content-neutral way Unlike alert data tools (eg, intrusion detection systems), which rely on triggering a threshold or matching a pattern, session data applications collect everything they see The ability to track everything, or everything up to the limits of your session application, is invaluable for tracking stealthy intruders From a network investigation standpoint, full content data is more valuable than session data Full content data can be sliced and diced in any number of ways by multiple tools But because collecting full content data can be nearly impossible on high-traffic links, we turn to session data as the next best approximation of conversations between networked parties Support for session data collection is built in to many common pieces of networking infrastructure Session data has seen more use for accounting and billing purposes, but many analysts are realizing the security aspects of this important form of network audit data, especially as traffic loads increase Some thoughtful pioneers have been using session data for years to detect intruders Since the mid-1990s the Air Force Computer Emergency Response Team's (AFCERT) Automated Security Incident Measurement (ASIM) sensors have collected session data Analysts have queried or manually perused records of exchanges between Air Force systems and the world, finding novel intrusions not detected by ASIM's signature-matching logic In the late 1990s, Paul Myrick, then an officer in the Air Force Communications Agency, wrote a tool called Profiler to independently collect session data at Air Force bases In 1993, Carter Bullard, then with Carnegie Mellon, began work on the Audit Record Generation and Utilization System (Argus) program There are many ways to use session data on operational networks, but this book focuses on security We take an even more narrow approach when we use session data to track the activities of intruders We care about the intruder's IP address and the protocols he or she uses; we don't care so much about the bandwidth the intruder consumed I present enough information to get the tools running under normal circumstances, and then help decipher their output.

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In the next chapter, we'll look at statistical data about network traffic These statistics won't be used to predict activity, as is the case with mathematical probability cases Rather, we'll use statistical tools to get an overview of the sorts of traffic seen by a network probe Just as session data is a step above the detail of full ontent data, statistical data is a way of moving about session data to understand network activity.Statistical data is the final form of content-neutral NSM information presented in this book Like full content and session data, statistics are collected to identify and validate intrusions Statistics are the ultimate evolution beyond the granularity of full content data and the vital traffic elements of session data This book discusses descriptive statistics, a way to summarize a collection of data in a clear and coherent manner We may track the amount of traffic on port 53 UDP seen over a two-week period or the amount of bandwidth consumed by the "top talker" at the present moment This is in contrast to inferential statistics, where analysts draw inferences on a opulation based on examining a sample of that population Because we will work with descriptive statistics, we will not be talking about confidence intervals or hypothesis testing I am more interested in looking at network traffic in a descriptive way, not wondering if my slice of traffic is representative of the Internet as a whole. In Visual Basic .NET Using Barcode creation for .Related: Barcode Generating Crystal VB.NET , RDLC VB.NET Barcode Generating , Generate Barcode .NET SDK

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Abstract excerpt: "Many Cisco routers and switches support NetFlow services which provides a detailed source of data about network traffic The Office of Information Technology Enterprise Networking Services group (OIT/ENS) at The Ohio State University (OSU) has written a suite of tools called flow-tools to record, filter, print and analyze flow logs derived from exports of NetFlow accounting records We use the flow logs for general network planning, performance monitoring, usage based billing, and many security related tasks including incident response and intrusion detection This paper describes what the flow logs contain, the tools we have written to store and process these logs, and discusses how we have used the logs and the tools to perform network management and security functions at OSU We also discuss some related projects and our future plans at the end of the paper" Significance: This paper cites a lot of earlier work on Cisco NetFlow records, but I think it coherently addresses many of the key issues It describes the Flow-tools package with examples and usage suggestions Highlights: Like the AT&T sensor architecture paper, this document also addresses privacy It acknowledges that content data is sometimes unnecessary when sessions are recorded: "The flow logs do not contain a record of what is usually considered the contents of the packets This means that although we could determine that a given host accessed a given web server at a certain time, the flow logs would not contain a record of the URL requested or the response received However, if you can correlate the activity recorded in the flow logs against the data in other logs (such as authentication logs), you might be able to match accounts (and so, to a large degree, people) to IP addresses, IP addresses to their associated network activity, and then match that network activity to specific details such as URLs requested, email addresses for correspondents, newsgroups read and so on Consequently, the act of recording and archiving NetFlow records raises a number of privacy concerns Our rationale is that the logs are invaluable for security, performance and network monitoring and usage based billing We could aggregate the data and use that for some of these functions, which would solve most of the privacy concerns However, having a long (2 to 3 month) window of past logs is invaluable for incident response, and we expect that it may prove invaluable for bill dispute resolution as well We think that the level of detail present in he flow logs represents an acceptable balance between utility and privacy for our environment On the positive side, we have found that we have had to do content based sniffing (eg, with tcpdump) far less often, since we have a ready source of information about network activity".

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