Tribeca: A System for Managing Large Databases of Network Traffic
نویسندگان
چکیده
The engineers who analyze tra c on high bandwidth networks must lter and aggregate either recorded traces of network packets or live tra c from the network itself. These engineers perform operations similar to database queries, but cannot use conventional data managers because of performance concerns and a semantic mismatch between the analysis operations and the operations supported by commercial DBMSs. Tra c analysis does not require fast random access, transactional update, or relational joins. Rather, it needs fast sequential access to a stream of tra c records and the ability to lter, aggregate, de ne windows, demultiplex, and remultiplex the stream. Tribeca is an extensible, stream-oriented DBMS designed to support network tra c analysis. It combines ideas from temporal and sequence databases with an implementation optimized for databases stored on high speed ID-1 tapes or arriving in real time from the network. The paper describes Tribeca's query language, executor and optimizer as well as performance measurements of a prototype implementation.
منابع مشابه
Tribeca: a System for Managing Large Databases of Network Traac
The engineers who analyze traac on high band-width networks must lter and aggregate either recorded traces of network packets or live traac from the network itself. These engineers perform operations similar to database queries, but cannot use conventional data managers because of performance concerns and a semantic mismatch between the analysis operations and the operations supported by commer...
متن کاملTribeca: A Stream Database Manager for Network Traffic Analysis
High speed computer and telephone networks carry large amounts of data and signalling traffic. The engineers who build and maintain these networks use a combination of hardware and software tools to monitor the stream of network traffic. Some of these tools operate directly on the live network; others record data on magnetic tape for later offline analysis by software. Most analysis tasks requi...
متن کاملBehavioral Analysis of Traffic Flow for an Effective Network Traffic Identification
Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...
متن کاملAnomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors
Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...
متن کاملPolicy Model for Sharing Network Slices in 5G Core Network
As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of netwo...
متن کامل