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In the context of networking, a heavy hitter is an entity in a data stream whose amount of activity (such as bandwidth consumption or number of connections) is higher than a given threshold. Detecting heavy hitters is a critical task for network management and security in the Internet and data centers. Data streams in modern network usually contain millions(More)
High-speed IP lookup remains a challenging problem in next generation routers due to the ever increasing line rate and routing table size. The evolution towards IPv6 results in long prefix length, sparse prefix distribution, and potentially very large routing tables. In this paper we propose a memory-efficient IPv6 lookup engine on Field Programmable Gate(More)
Machine learning (ML) algorithms have been shown to be effective in classifying the dynamic internet traffic today. Using additional features and sophisticated ML techniques can improve accuracy and can classify a broad range of application classes. Realizing such classifiers to meet high data rates is challenging. In this paper, we propose two(More)
Hash tables are widely used in many network applications such as packet classification, traffic classification, and heavy hitter detection, etc. In this paper, we present a pipelined architecture for high throughput online hash table on FPGA. The proposed architecture supports search, insert, and delete operations at line rate for the massive hash table(More)
Traffic classification is a critical task in network management. Decision-trees are commonly used in Machine Learning (ML)-based traffic classification algorithms. Most of the existing implementations are hardware-based, while a new trend for network applications is to use software-based solutions. Since the decision-tree used for traffic classification is(More)
Detecting heavy hitters is essential for many network management and security applications in the Internet and in data centers. Heavy hitter is the entity in a data stream whose amount of activity, such as bandwidth consumption or number of connections is higher than a given threshold. In this work, we propose a pipelined architecture for an online heavy(More)
Detecting heavy activity aggregation in data streams is a critical task for many networking, data base and data-mining applications. The aggregation points often belong to hierarchical domains (e.g. IP domain, XML data tree, etc.). These aggregation points are referred to as hierarchical heavy hitters. The hierarchical domains is usually very large with(More)
Statistical information of network traffic flows is essential for many network management and security applications in the Internet and data centers. In this work, we propose an architecture for a dynamically configurable online statistical flow feature extractor on FPGA. The proposed architecture computes a set of widely used statistical features of the(More)
Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum(More)
Significant changes in traffic patterns often indicate network anomalies. Detecting these changes rapidly and accurately is a critical task for network security. Due to the large number of network users and the high throughput requirement of today's networks, traditional per-item-state techniques are either too expensive when implemented using fast storage(More)