38516500
A New Approach of Network Intrusion Detection Using HVDM-Based SOM
The research of applying Self-organizing Maps for intrusion detection is investigated in this paper. A novel approach is presented for enhancing SOM's abilities of identifying temporal network attacks, which combine with FIR filter. Meanwhile, we reconsider the heterogeneous dataset that composed of network connection's features, and select HVDM as the distance function determining the winning neuron during SOM's training and testing. In the end, KDD benchmark dataset is employed to validate the efficiency of our approach, and the results is detection rates of 96.5%, false positive rates of 6.2%, which accounts for good performance of our new approach in intrusion detection fields.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
36695959
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.
1135112
Measurement and Statistics of Application Business in Complex Internet
Owing to independent topologies and autonomic routing mechanism, the logical networks formed by Internet application business behavior cause the significant influence on the physical networks. In this paper, the backbone traffic of TUNET (Tsinghua University Networks) is measured, further more, the two most important application business: HTTP and P2P are analyzed at IP-packet level. It is shown that uplink HTTP and P2P packets behavior presents spatio-temporal power-law characteristics with exponents 1.25 and 1.53 respectively. Downlink HTTP packets behavior also presents power-law characteristics, but has more little exponents γ = 0.82 which differs from traditional complex networks research result. Moreover, downlink P2P packets distribution presents an approximate power-law which means that flow equilibrium profits little from distributed peer-to peer mechanism actually.
16013630
Runtime Performance of Parallel Array Assignment: An Empirical Study
Generating code for the array assignment statement of High Performance Fortran (HPF) in the presence of block-cyclic distributions of data arrays is considered difficult, and several algorithms have been published to solve this problem. We present a comprehensive study of the run-time performance of the code these algorithms generate. We classify these algorithms into several families, identify several issues of interest in the generated code, and present experimental performance data for the various algorithms. We demonstrate that the code generated for block-cyclic distributions runs almost as efficiently as that generated for block or cyclic distributions.
5908339
Intelligent Hierarchical Intrusion Detection System for Secure Wireless Ad Hoc Network
The nature of mobile ad hoc network (MANETs) makes them vulnerable to security attacks. Intrusion detection is a vital secure scheme for wireless mobile ad hoc networks (MANETs). However, the generalizing ability of current IDS is poor when given less priori knowledge, and a centralized intrusion detection network security solution does not work well in MANETs. In this paper we discuss the characters of security issues in ad hoc networks and propose a Support Vector Machine (SVM) based distributed hierarchical intrusion detection system that adapt to the characters of current ad hoc networks. Performance evaluations based on simulation experiment manifested our proposed approach is efficient and effective.
7324862
A New Description Language for Data-Driven Asynchronous Circuits and its Design Flow
A lot of up-to-date asynchronous circuit design tools are based on the control-driven asynchronous circuit style, such as Balsa and Haste/Tangram. The control-driven approach will limit the performance of the result circuits. To improve the performance of the asynchronous circuits, a new description language - Biscotti, which is suitable for the data-driven asynchronous circuit design, has been introduced in this paper. And a data-driven asynchronous circuit design flow is also be presented here. The experimental results show that this design flow is a fast and efficient way to design the data-driven asynchronous circuit, which can achieve higher performance compared with the control-driven design approach.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance