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We optimize flow placement for a hybrid network implementing an adaptive neural network classifier. We predict elephant flows with high accuracy on anonymized university network traffic. We also demonstrate the capability to perform highly complex actions at 40 Gbps using less than 5% of co-processor capacity. This shows that it is possible to implement intelligent actions such as a neural network...
The problem of differentiating regular from anomalous traffic has been studied extensively. However, the classification of anomalous traffic to different types of attacks remains a difficult and widely unexplored area. In the age of big data analytics it is becoming paramount to automate the security process, such as reaction to attacks and mitigation based on their type. This paper investigates the...
Anomalies in computer networks has increased in the last decades and raised concern to create techniques to identify these unusual traffic patterns. This research aims to use data mining techniques in order to correctly identify these anomalies, particularly in spam detection, for it was applied an collection of machine learning algorithms for data mining tasks and an dataset called SPAMBASE to identify...
A Computer network or data communication network is a telecommunication network that allows computers to exchange data. Computer networks are typically built from a large number of network devices such as routers, switches and numerous types of middle boxes with many complex protocols implemented on them. They need to accomplish very complex tasks with access to very limited tools. As a result, network...
Challenges on the Internet infrastructure such as Distributed Denial of Service (DDoS) attacks are becoming more and more elaborate. DDoS attacks have emerged as a growing threat not only to businesses and organizations but also to national security and public safety. DDoS attacks have become more dynamic and intelligent than before, prompting equally advanced responses for dealing with these attacks...
Packet classification is an important topic for high speed routers nowadays. There are many packet classification algorithms based on decision tree like Hicuts, Hyper cuts and Hyper split. Because Hicuts and Hyper cuts divides the rule sets by cutting the address space into equal-sized subspaces, their cutting efficiency is not good. Although Hyper split proposed a good end-point-based cutting scheme,...
This paper presents an approach to speed up and enhance matching of virtual network requests to available resources in virtual network provisioning frameworks. The method consists of introducing a weight or score expressing the importance of the resources, their attributes and the values taken by these attributes. The scores are obtained through statistical analysis of the requests for virtual network...
Archives of computer-mediated communication (CMC) could be valuable organizational resources. This paper describes an automatic approach that facilitates users' integrated understanding of discussion and behavior of CMC participants. In the presented parallel learning framework the global population is divided into some subpopulations, each assigned to a distinct processor. Each subpopulation consists...
The frequent items problem is to process a stream as a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Aiming at higher false positive rate of the Space-Saving algorithm, an LRU-based (Least Recently Used, LRU) improved algorithm with low frequency item pre-eliminated...
Accurate traffic classification is critical in network security monitoring and traffic engineering. To overcome the deficiencies of traditional traffic classification methods with port mapping and signature matching, several machine learning techniques were proposed. However, there are two main challenges for classifying network traffic using machine learning method. Firstly, labeled samples are scarce...
In this paper, we propose a novel localization algorithm in Wireless Sensor Network. The proposed method is a kind of range-free localization algorithm which only uses the proximity information between sensor nodes. In the proposed method, a robust weighted algorithm is presented to calculate the average hop distances between sensor nodes and anchor nodes. The proposed method is applied to an isotropic...
Decrease in hardware costs and advances in computer networking technologies have led to increased interest in the use of grid computing systems. One of the biggest issues in such systems is the development of effective techniques/algorithms for the distribution of the jobs/load of a grid application on multiple resources to achieve goals such as minimizing execution time, minimizing communication...
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection...
Community detection is always an outstanding problem in the study of networked systems such as social networks and computer networks. In this paper, a novel method based on particle swarm optimization is proposed to detect community structures by optimizing network modularity. At the beginning, an improved spectral method is used to transform community detection into a cluster problem and the weighted...
Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have...
Traditional application identification based on port numbers has become increasingly inaccurate. A more accurate alternative is to inspect the application payloads of traffic flows. The main drawback of such method is that target applications must be manually analyzed beforehand. Another alternative is to exploit the distinctive statistical properties of traffic flows and apply machine learning techniques...
In this article, we proposed an intrusion prevention system, named cumulative-sum-based intrusion prevention system (CSIPS) which detects malicious behaviors, attacks and distributed attacks launched to remote clients and local hosts based on the cumulative sum (CUSUM) algorithm. Experimental results show that CSIPSs in a united defense environment can carry out a higher security level for the environment.
As line rates increase, the task of designing high performance architectures with reduced power consumption for the processing of router traffic remains important. In this paper, we present a multi-engine packet classification hardware accelerator, which gives increased performance and reduced power consumption. It follows the basic idea of decision-tree based packet classification algorithms, such...
The process of categorizing packets into flows in an internet router is called packet classification. All packets belonging to the same flow obey pre-defined rules and are processed in a similar manner by the router. Packet classification is needed for non best-effort services, such as firewalls and quality of service, services that require the capability to distinguish and isolate traffic in different...
Intrusion detection systems constitute a crucial cornerstone in securing computer networks especially after the recent advancements in attacking techniques. IDSes can be categorized according to the nature of detection into two major categories: signature-based and anomaly-based. In this paper we present KBIDS, a kernel-based method for an anomaly-based IDS that tries to cluster the training data...
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