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While more detail can be extracted by many of the tools presented in this chapter, these six elements are the 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 ormal circumstances, and then help decipher their output. word automation services sharepoint 2013 convert to pdf c# How To Convert Microsoft Word ( Docx /Doc) To PDF In C# with ...
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So far we've discussed two forms of network-based information used to identify and validate intrusions First we explored full content data, in which every element of a packet is available for analysis In some cases we care about header details, but more often the application content is what we need Next we looked at session data, where conversations between parties are summarized to include IP addresses, ports, protocols, timestamps, and counts of data transferred Analyzing session data is the easiest way to track the timing and movements of some intruders Collection of both forms of data is content neutral; we generate full content or session data regardless of the information conveyed by those forms We can limit the field of view of full content or session data tools through BPFs and other configuration mechanisms Such limitations are imposed in high-traffic environments or used to focus attention on a specific IP address, port, or protocol Even within those constraints, collection is still a content-neutral affair A BPF of udp and port 53 to Tcpdump will catch a port 53 UDP-based back door and also collect normal DNS traffic Full content data is the most content-neutral information source The full raw packet collected by a sensor is more or less the same packet transmitted by the originating party, albeit with alterations at layer 2 to accommodate changing MAC addresses If packets have been fragmented near the source but reassembled by a scrubbing firewall (eg, via OpenBSD's Pf), the reassembled packet is less similar to the one originally sent Overall, full content data is a fairly accurate representation of the information originally conveyed by the parties to a communication Session data is also content neutral; session data tools summarize the onversations they see without regard to the content of a communication Session data, especially in the form of unidirectional flows (eg, NetFlow), is less granular than full content data The more characteristics of a conversation the session tool records, the closer its output resembles full content data Session data represents a trade-off, where vital traffic elements like IPs, ports, and protocols are saved at the expense of details like sequence numbers and other TCP/IP fields. 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