11982779
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent overfitting the training data just like decision tree learning. The intrusion detection method is an anomaly detection method and the feature is selected from the packets. In the experiments, we first check the ability of the neural network and then use it to perform detection in a WLAN. The results show that it can detect new intrusion behavior and some improving methods are presented in the conclusions
7090731
Anomaly Intrusion Detection Methods for Peer-to-Peer System
Peer-to-peer systems and applications are the hotspot of research of network applications. As peer-to-peer system has no central system and is deployed on an open network, new concerns regarding security have been raised. As an additional security measure, the intrusion detection system would help determine whether unauthorized users are attempting to access, have already accessed, or have compromised the network Intrusion detection, as the second line of defense, is an indispensable tool for highly survivable networks. In this paper two anomaly intrusion detection methods are proposed for peer-to-peer system. The main characters of the methods are that they can detect intrusion in real-time without any expert knowledge and attack signatures. One method uses hidden Markov model to check reflector DoS attacks, another based on adaptive resonance theory, which can learn the normal behavior with unsupervised method. The experimental P2P system is built on FreePastry 1.401 and JDK 1.5.0. The results have indicated that the methods can find DoS attacks immediately and find new intruders with low false alarm rate.
10205775
Kernel-based Global Folley-Sammon Discriminate Analysis
Kernel trick is a useful method dealing with the nonlinearity of patterns ' distribution, and widely used for non-linear pattern classification problem. Recently, many kernel-based Fisher discriminate methods were proposed, the features extracted by those methods are statistically uncorrelated, usually ineffectively when used in so called "small sample problem "(SSS), which exists in most FR tasks. To overcome this shortcoming, in this paper we present Kernel-based Global Folley-Sammon Discriminate Analysis (KGFS), it can be realized in three steps: firstly by combing kernel function, map data onto an implicit high-dimensional feature space, secondly construct orthogonal feature space there, finally calculate iterative global orthogonal discriminate vectors. Experiments have been done on ORL dataset. In terms of classification error rate performance, proposed kernel based method KGFS has a better performance than other three commonly used kernel-based methods, such as GDA, MGDAandKDDA.
12386699
Free parallel data mining
Data mining is computationally expensive. Since the benefits of data mining results are unpredictable, organizations may not be willing to buy new hardware for that purpose. We will present a system that enables data mining applications to run in parallel on networks of workstations in a fault-tolerant manner. We will describe our parallelization of a combinatorial pattern discovery algorithm and a classification tree algorithm. We will demonstrate the effectiveness of our system with two real applications: discovering active motifs in protein sequences and predicting foreign exchange rate movement.
45662594
Neural Network Based Kinematic Control of the Hyper-Redundant Snake-Like Manipulator
In a sinusoid like curve configuration, the snake-like manipulator (also called snake arm) has a wide range of potential applications for its redundancy to overcome conventional industrial robot's limitation when carrying out a complex task. It can perform many kinds of locomotion like the nature snake or the animal's tentacle to avoid obstacles, follow designated trajectories, and grasp objects. Effectively control of the snake-like manipulator is difficult for its redundancy. In this study, we propose an approach based on BP neural network to kinematic control the hyper-redundant snake-like manipulator. This approach, inspired by the Serpenoid curve and the concertina motion principle of the nature snake, is completely capable of solving the control problem of a planar snake-like manipulator with any number of links following any desired direction and trajectory. With shape transformation and base rotation, the manipulator's configuration changes accordingly and moves actively to perform the designated tasks. By using BP neural networks in modeling the inverse kinematics, this approach has such superiorities as few control parameters and high precision. Simulations have demonstrated that this control technique for the snake-like manipulator is available and effective.
30055456
A new block detection receiver for noncoherent orthogonal CDMA system
A new block detection receiver is presented for noncoherent orthogonal CDMA (such as the CDMA IS-95A reverse link). This new receiver uses multiple Hadamard (or Walsh) codes for joint detection instead of using a single Walsh code as conventional receivers do. The simulation results show that the new block detection receiver is significantly better than the conventional non-block detection receivers in both additive white Gaussian noise and flat Rayleigh fading channels. A reduced-complexity algorithm for the block detection is also presented with significant complexity reduction and negligible performance degradation.
5438961
A dynamic multiple-threshold bandwidth reservation (DMTBR) scheme for QoS provisioning in multimedia wireless networks
Next-generation wireless networks target to provide quality of service (QoS) for multimedia applications. We study the wireless systems that support two QoS requirements: keeping the handoff dropping probability less than a predefined QoS threshold while maintaining relative priorities for different traffic classes based on blocking probability. To achieve this goal, a dynamic multiple-threshold bandwidth reservation (DMTBR) scheme, which is capable of granting differential priorities to different traffic classes and to new and handoff traffic for each class by dynamically adjusting bandwidth reservation thresholds, is proposed. In this scheme, the thresholds are obtained in two steps. The initial values are estimated based on instantaneous network traffic situation, then the thresholds will be further adapted according to the instantaneous network QoS status. In times of network congestion, a preventive measure is taken to throttle the new connections. Another contribution of this paper is to generalize the concept of relative priority and hence give the network operator more flexibility to adjust admission control policy by taking into account some dynamic factors such as offered load. The extensive simulations are conducted for two purposes. First, we verify the performance of the proposed scheme and show our scheme performs well under various traffic loads. Second, we demonstrate that the DMTBR scheme gains more advantages when taking the offered load into consideration.
33522682
Influence of connecting units' thicknesses on tandem organic devices' performances
The present work investigates the influence of the Alq"3:Mg and MoO"3 thicknesses in the connecting unit on the performance of tandem organic light-emitting devices (OLEDs). By systematically varying the Alq"3:Mg and MoO"3 thicknesses, we obtained a higher current efficiency of 37.3cd/A for a device with 30nm Alq"3:Mg and 3nm MoO"3 layer as connecting units. The optimal device performance is enhanced by at least 14%, compared with those of devices we fabricated in this paper. It suggests that appropriate Alq"3:Mg and MoO"3 thicknesses can enhance the charge generating ability for connecting units. On the other hand, it was found that the charge transporting layer would decrease strongly because of much thicker or thinner MoO"3 thicknesses. The results demonstrate that it is an effective method to improve the performance of OLEDs by using a optimal thickness for Alq"3:Mg and MoO"3 layers.