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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".

Like all Eclipse projects, WTP is built from a collection of plug-ins However, the plug-ins are not organized in a flat structure Instead, they depend on each other in a very controlled way The plug-ins can be organized into layers such that plug-ins in one layer do not depend on plug-ins in the layers above them This layering of plug-ins is an important design characteristic since it decouples the layers from each other Lower layers can be built and used independently of upper layers Ideally, the dependencies between layers are through well-defined, stable, platform APIs so that the implementation of lower layers can be changed without affecting the upper layers The definition of platform APIs in WTP is a major goal and will be achieved incrementally in future releases The bottom layer is the Eclipse Platform WTP uses many APIs in this layer, but has notable dependencies on the Java Development Tools (JDT) and the Debug APIs J2EE projects are Java projects with additional structure, so the dependency on JDT is clear The dependency on the Debug APIs comes through the need to debug J2EE components in application servers and to source debug JSPs WTP supports JSP debugging through JSR 45, which is the standard for debugging non-Java source in a JVM Next is the Java Tools layer WTP makes use of three components: Eclipse Modeling Framework (EMF), Graphical Editing Framework (GEF), and Java Edit Model (JEM) EMF is used as the basis for defining the models provided by WTP For example, JST provides EMF models for all J2EE deployment descriptors, such as webxml and applicationxml The graphical tabs in the XSD and WSDL editors use GEF JEM was part of the Visual Editor (VE) subproject and provides a higher-level API for accessing Java source code JEM extends the Java model provided by JDT The WTP download page contains links to the correct versions of EMF, GEF, and JEM for each WTP build Final WTP releases also include an all-in-one zip that bundles these three components and the Eclipse platform The third layer is WST As just described, WST contains no J2EE dependencies It can be used for Web page, XML, Data, and Web service development The WTP development team plans to define Eclipse features for each of these functional groupings WST is suitable as a basis for IDEs that support other programming technologies such as NET or LAMP The top layer is JST, which extends WST For example, the JSP editor extends the HTML editor, the J2EE Web services tools extend the XML Web service tools, and the 2EE application server support extends the core server support The WTP development team plans to define Eclipse features for J2EE functional groupings such as JSP, servlet, EJB, and Web services.

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There are many means of implementing protocol anomaly detection Most of the variation depends on how much detail is monitored nd how much state is maintained Protocol anomaly detection is built on a foundation of pattern matching What differentiates it from explicit matching (ie, signature systems) is the kind of patterns used In most cases, protocol anomaly detection also requires some sort of stateful protocol-aware matching system, without which it can be very prone to false positives While a very simplistic implementation of protocol anomaly detection may only look for a small number of known problematic conditions, such as overlong buffers and questionable encoding, a more complete implementation can track every transition and evaluate all data for compliance The tradeoff is typically in speed or execution time because the more detail an implementation tracks, the more comparisons it must perform at each stage However, a more complete implementation also allows for detection of a wider range of anomalies From a state maintenance perspective, a very simplistic implementation may maintain some basic state about a given flow, such as open, authenticated and so on More complete implementations, however, can maintain a complete transactional history of a given flow The tradeoff here is in storage capacity More state requires more storage, but it also allows an implementation to draw more complex conclusions and detect more subtle anomalies One of the most complete forms of protocol anomaly detection is application protocol modelling In this form, a model of a given protocol is created from the protocol specification and some study of the various implementations This model is then used by a system designed to track a protocol flow and compare it to the model While it is possible to perform protocol anomaly detection in many stateful, protocol-aware systems (ie, "stateful inspection engines"), the limitations on the amount of state stored and the patterns matched often create a fairly lightweight implementation This is usually the result of an attempt to use a framework designed for explicit matching for anomaly detection While it can generate some results, it will never be as complete as something designed for the type of matching used by an anomaly detection system.

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Another term that is often used when discussing architecture is infrastructure It is helpful to look at a general de nition of infrastructure from Wikipedia: Infrastructure typically refers to the technical structures that support a society, such as roads, water supply, wastewater, power grids, ood management systems, telecommunications (Internet, telephone lines, broadcasting), and so forth Most of these systems are typically owned and managed by governments or public utility companies These various elements may collectively be termed civil infrastructure Viewed functionally, infrastructure facilitates the production of goods and services; for example, roads enable the transport of raw materials to the production plant and distribution of nished products to markets More speci cally, infrastructure for the data warehouse is the foundation technology that supports the overall environment This includes basic networking services such as providing shared network drives for storing the group s les, such as word processing, spreadsheets, and presentations The existence of some sort of computer for each user can also be considered infrastructure This is usually put in place when an employee is hired The data warehouse project does not need to order laptops so that each business analyst has his or her own machine Basic networking of computers, via a local area or wireless network, is another example of infrastructure Each project team does ot need to design and install the infrastructure, but should ensure that there is enough capacity in the infrastructure to support the new data warehousing, reporting, and analytical functions. Creation In .NET Framework Using Barcode maker for . Technical Architecture in Action. Encode EAN / UCC .Related: Code 128 Generator Word , .NET UPC-E Generator , Generate ITF-14 C#

5 Printer In Java Using Barcode maker for Java Control to generate, create UPC-A . We conclude this chapter on full content tools by discussing one of the greatest open source networking tools available: Ethereal Detail-oriented readers robably observed that most of the Ethereal screenshots that appear in this book were made using the Windows version of Ethereal This is proof that UNIX can serve as the ultimate capture platform, while Windows can act as an adequate analysis platform With an entire book on Ethereal already on the shelves, I will lay out the essentials for how I use Ethereal to analyze full content data I turn to Ethereal when I need to quickly browse through a subset of packets I never load a trace file bigger than a few megabytes As we'll see in forthcoming chapters, there are better techniques for analyzing traffic than taking a packet-by-packet approach Ethereal's strength lies in its decoding ability and its potential for rapid visual comparisons When hundreds or thousands of megabytes of packets need to be understood, turn to session data as explained in 7.Related: Print EAN-8 .NET , UPC-E Generator .NET , .NET ISBN Generation

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