Vulnerabilities in web applications expose computer networks to security threats. For example, attackers use a large number of normal user websites as hopping sites, which are illegally operated using malware distributed by abusing vulnerabilities in web applications on these websites, for attacking other websites and user terminals. Thus, the security threats, resulting from vulnerabilities in web applications prevent service providers from constructing secure networking environments. To protect websites from attacks based on the vulnerabilities of web applications, security vendors and service providers collect attack information using web honeypots, which masquerade as vulnerable systems. To collect all accesses resulting from attacks that include further network attacks by malware, such as downloaders, vendors and providers use high-interaction web honeypots, which are composed of vulnerable systems with surveillance functions. However, conventional high-interaction web honeypots can collect only limited information and malware from attacks, whose paths in the destination URLs do not match the path structure of the web honeypot since these attacks are failures. To solve this problem, we propose a scheme in which the destination URLs of these attacks are corrected by determining the correct path from the path structure of the web honeypot. Our Internet investigation revealed that 97% of attacks are failures. However, we confirmed that approximately 50% of these attacks will succeed with our proposed scheme. We can use much more information with this scheme to protect websites than with conventional high-interaction web honeypots because we can collect complete information and malware from these attacks.

Frequency-domain equalization (FDE) based on minimum mean square error (MMSE) is considered as a promising equalization technique for a broadband single-carrier (SC) transmission. When a square-root Nyquist filter is used at a transmitter and receiver to limit the signal bandwidth, the presence of timing offset produces the inter-symbol interference (ISI) and degrades the bit error rate (BER) performance using MMSE-FDE. In this paper, we discuss the mechanism of the BER performance degradation in the presence of timing offset. Then, we propose joint MMSE-FDE & spectrum combining which can make use the excess bandwidth introduced by transmit filter to achieve larger frequency diversity gain while suppressing the negative effect of the timing offset.

Quantization is an important operation in digital communications systems. It not only introduces quantization noise but also changes the statistical properties of the quantized signal. Furthermore, quantization noise cannot be always considered as an additive source of Gaussian noise as it depends on the input signal probability density function. In orthogonal-frequency-division-multiplexing transmission the signal undergoes different operations which change its statistical properties. In this paper we analyze the statistical transformations of the signal from the transmitter to the receiver and determine how these effect the quantization. The discussed process considers the transceiver parameters and the channel properties to model the quantization noise. Simulation results show that the model agrees well with the simulated transmissions. The effect of system and channel properties on the quantization noise and its effect on bit-error-rate are shown. This enables the design of a quantizer with an optimal resolution for the required performance metrics.

Admission control is a procedure to guarantee a given level of Quality of Service (QoS) by accepting or rejecting arrival connection requests. There are many studies on backlog or loss rate evaluation formulas for admission control at a single node. However, there are few studies on end-to-end evaluation formulas suitable for admission control. In a previous paper, the authors proposed a new stochastic network calculus for many flows using an approach taken from large deviations techniques and obtained asymptotic end-to-end evaluation formulas for output burstiness and backlog. In this paper, we apply this stochastic network calculus to a heterogeneous tandem network with many forwarding flows and cross traffic flows constrained by leaky buckets, and obtain a simple evaluation formula for the end-to-end backlog. In this formula, the end-to-end backlog can be evaluated by the traffic load at the bottle neck node. This result leads us to a natural extension of the evaluation formula for a single node.

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

This letter introduces a blind minimum interference symbol synchronization for orthogonal frequency-division multiplexing (OFDM) systems based on the cyclic prefix (CP). The basic idea of our contribution is to obtain an estimate of the channel-tap powers from the correlation characteristics of the CP. Based on the estimate of the channel-tap powers, a minimum interference metric is proposed. The proposed algorithm has low complexity and can be used to cope with long inter-symbol-interference (ISI) channels with length up to twice the CP length.

In this paper, we address adaptive link switching over heterogeneous wireless access networks including IEEE 802.11. When an IEEE 802.11 link is congested, the transmission link of a terminal with multi-RATs (radio access technologies) is switched to another radio access systems. To this end, we propose link-level metrics of LC (link cost) and AC (access cost) for quantifying TCP congestion over IEEE 802.11 networks. The proposed metric can be easily measured at a local wireless terminal, and that enables each multi-RAT terminal to work in a distributed way. Through various indoor and outdoor experiments using a test-bed system, we verify that the proposed link level metrics are good indicators of TCP traffic congestion. Experimental results show that the proposed metrics can detect congestion occurrence quickly, and avoid the TCP throughput degradation of other neighboring terminals, when they are used for transmission link switching.

This paper presents an improved Gini-Index algorithm to correct feature-selection bias in text classification. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, an improved Gini-Index algorithm for feature selection, designed for text categorization and based on Gini-Index theory, was introduced, and it has proved to be better than the other methods. However, we found that the Gini-Index still shows a feature selection bias in text classification, specifically for unbalanced datasets having a huge number of features. The feature selection bias of the Gini-Index in feature selection is shown in three ways: 1) the Gini values of low-frequency features are low (on purity measure) overall, irrespective of the distribution of features among classes, 2) for high-frequency features, the Gini values are always relatively high and 3) for specific features belonging to large classes, the Gini values are relatively lower than those belonging to small classes. Therefore, to correct that bias and improve feature selection in text classification using Gini-Index, we propose an improved Gini-Index (I-GI) algorithm with three reformulated Gini-Index expressions. In the present study, we used global dimensionality reduction (DR) and local DR to measure the goodness of features in feature selections. In experimental results for the I-GI algorithm, we obtained unbiased feature values and eliminated many irrelevant general features while retaining many specific features. Furthermore, we could improve the overall classification performances when we used the local DR method. The total averages of the classification performance were increased by 19.4 %, 15.9 %, 3.3 %, 2.8 % and 2.9 % (kNN) in Micro-F1, 14 %, 9.8 %, 9.2 %, 3.5 % and 4.3 % (SVM) in Micro-F1, 20 %, 16.9 %, 2.8 %, 3.6 % and 3.1 % (kNN) in Macro-F1, 16.3 %, 14 %, 7.1 %, 4.4 %, 6.3 % (SVM) in Macro-F1, compared with tf*idf, χ2, Information Gain, Odds Ratio and the existing Gini-Index methods according to each classifier.

'Super Hi-Vision' (SHV) is promising as a future form of television. It is an ultra-high definition TV system that has 16 times the number of pixels of HDTV and employs a 22.2 multichannel sound system. It offers superior presence and gives the impression of reality. The information bitrates of the current prototypes range from 24 to 72 Gbit/s, and a fiber optic transmission system is needed to transfer even just one channel. This paper describes the optical transmission technologies that have been developed for SHV inter-equipment connects and links between outdoor sites and broadcasting stations.

This paper discusses a simple and speedy routing method in large-capacity optical Wavelength Division Multiplexing (WDM) networks. The large-capacity WDM network is necessary to accommodate increasing traffic load in future. In this large-capacity WDM network, each link has many fibers and a huge amount of optical data can be transmitted through these fibers simultaneously. Optical path is configured for transmitting optical data by wavelength reservation including routing and wavelength assignment (RWA). Since traditional RWA methods have to treat much information about available wavelengths in each fiber, it is difficult to resolve RWA problem on time. In other words, the electrical processing becomes the bottleneck in the large-capacity WDM network. Therefore, a simple and speedy RWA method is necessary for the large-capacity WDM network. In this paper, we propose the simple and effective RWA method which considers reduced information as Network Map. The objective is to improve the network performance by using multiple fibers effectively. The complex processing is not suitable for data transmission because the switching operation must be done in very short time for one request. In addition to this, it is not practical to collect detailed network information frequently. The proposed wavelength assignment method assigns wavelength more uniformly than traditional method, and therefore, the proposed routing method can select routes without considering detailed information about each wavelength state. The proposed routing method needs only local information and reduced network information. This paper shows that the proposed routing method can get suitable solution for large-capacity optical WDM networks through computer simulations. The proposed RWA method drastically improves the loss probability against other simple RWA methods. This paper also describes two types of optical switches with tunable or fixed wavelength conversions. The wavelength converters with relatively low technology becomes effective with the proposed RWA method in the large-capacity WDM network. This paper reveals that complex routing methods are not necessary for large-capacity optical WDM networks.

Internet group-based application layer services such as the overlay networks and P2P systems can benefit from end-to-end network status information. An efficient and accurate bandwidth measurement technique plays an important role in acquiring this information. We propose an end-to-end bottleneck link capacity measurement technique that utilizes path signatures combined with graphical analyses. This feature reduces the probe overhead and decreases the convergence time. We used ns-2 simulations and actual Internet measurements, which resulted in a high level of accuracy and a short probe time with low overhead.

The excessive memory access required to perform motion compensation when decoding compressed video is one of the main limitations in improving the performance of an H.264/AVC decoder. This paper proposes an H.264/AVC decoder that employs three techniques to reduce external memory access events: efficient distribution of reference frame data, on-chip cache memory, and frame memory recompression. The distribution of reference frame data is optimized to reduce the number of row activations during SDRAM access. The novel cache organization is proposed to simplify tag comparisons and ease the access to consecutive 4×4 blocks. A recompression algorithm is modified to improve compression efficiency by using unused storage space in neighboring blocks as well as the correlation with the neighboring pixels stored in the cache. Experimental results show that the three techniques together reduce external memory access time by an average of 90%, which is 16% better than the improvements achieved by previous work. Efficiency of the frame memory recompression algorithm is improved with a 32×32 cache, resulting in a PSNR improvement of 0.371 dB. The H.264/AVC decoder with the three techniques is fabricated and implemented as an ASIC using 0.18 µm technology.

The Network-on-Chip (NoC) is limited by the reliability constraint, which impels us to exploit the fault-tolerant routing. Generally, there are two main design objectives: tolerating more faults and achieving high network performance. To this end, we propose a new multiple-round dimension-order routing (NMR-DOR). Unlike existing solutions, besides the intermediate nodes inter virtual channels (VCs), some turn-legally intermediate nodes inside each VC are also utilized. Hence, more faults are tolerated by those new introduced intermediate nodes without adding extra VCs. Furthermore, unlike the previous solutions where some VCs are prioritized, the NMR-DOR provides a more flexible manner to evenly distribute packets among different VCs. With extensive simulations, we prove that the NMR-DOR maximally saves more than 90% unreachable node pairs blocked by faults in previous solutions, and significantly reduces the packet latency compared with existing solutions.

Wireless regional area network (WRAN) is intended to offer the fixed wireless access services using cognitive radio technology in the TV white space. Therefore, WRAN shall minimize the transmission power so that harmful interference is not imposed on the licensed users operating in the TV bands. In this paper, we propose a processing block that offers improvements in the SNR and diversity gain using the block to algebraically process two constellation symbols. Thus, the transmission power can be reduced by an amount equal to the gains. The simulation result shows that the proposed scheme has a better bit error performance than the transmission scheme defined in the IEEE 802.22 draft standard.

Linear discriminant analysis (LDA) is one of the well-known schemes for feature extraction and dimensionality reduction of labeled data. Recently, two-dimensional LDA (2DLDA) for matrices such as images has been reformulated into symmetric 2DLDA (S2DLDA), which is solved by an iterative algorithm. In this paper, we propose a non-iterative S2DLDA and experimentally show that the proposed method achieves comparable classification accuracy with the conventional S2DLDA, while the proposed method is computationally more efficient than the conventional S2DLDA.

Device modeling techniques for high-frequency circuits operating at over 100 GHz are presented. We have proposed the bond-based design as an accurate high-frequency circuit design method. Because layout parasitic extractions (LPE) are not required in the bond-based design, it can be applied high-frequency circuit design at over 100 GHz. However, customized device models are indispensable for the bond-based design. In this paper, device modeling techniques for high-frequency circuit design using the bond-based design are proposed. The customized device model for MOSFETs, transmission lines and pads are introduced. By using customized device models, the difference between the simulated and measured gains of an amplifier is improved to less than 0.6 dB at 120 GHz.

A resource allocation scheme for multi-access MIMO-OFDM systems in uplink was developed to improve power and spectrum efficiency in the frequency and the space domains [1]. The scheme requires a multi-user detector in the receiver and assumes identical spatial crosscorrelation across all subcarriers for any pair of spatially separable users. However, the multi-user detection device may not exist in the receiver and the identical spatial crosscorrelation assumption may not be valid in some operational scenarios. The paper develops a scheme to remedy these problems for multi-access MIMO-OFDM systems without using multi-user detection techniques and the assumption. The proposed scheme aims at minimizing the total user transmit power while satisfying the required data rate, the maximum transmit power constraint, and the bit error rate of each user. The simulation results are presented to demonstrate the efficacy of the proposed algorithm.

A balanced push-push frequency doubler has been demonstrated in 0.25-µm SOI (Silicon on Insulator) SiGe BiCMOS technology operating from 22 GHz to 29 GHz with high fundamental frequency suppression and high conversion gain. A series LC resonator circuit is connected in parallel with the differential outputs of the doubler core circuit. The LC resonator is effective to improve the fundamental frequency suppression. In addition, the LC resonator works as a matching circuit between the output of the doubler core and the input of the output buffer amplifier, which increases the conversion gain of the whole circuit. A measured fundamental frequency suppression of greater than 46 dBc is achieved at an input power of -10 dBm in the output frequency band of 22-29 GHz. Moreover, maximum fundamental frequency suppression of 66 dBc is achieved at an input frequency of 13 GHz and an input power of -10 dBm. The frequency doubler works at a supply voltage of 3.3 V.

One high profile intra predictor generation engine is proposed in this paper. Firstly, hardware level algorithm optimization for intra 88 (I8MB) mode is introduced. The original candidate pixels for generating prediction samples of I8MB are replaced with boundary pixels of intra 44 (I4MB) blocks. Based on this adoption, full data reuse between predictors of I4MB and filtered samples of I8MB can be achieved with almost no quality loss. Secondly, one lossless two-44-block based parallel predictor generation flow is proposed. The original predictor generation flow is optimized from 16 stages to 10 stages for I4MB and Intra 1616 (I16MB), which saves 37.5% processing cycles. For I8MB, similar methodology with different processing order of 44 scaled blocks is introduced. Thirdly, fully utilized hardwired engines for I4MB, I16MB and I8MB are proposed in this paper. Except DC (direct current) and plane modes, full data reuse among all intra modes of high profile can be achieved. Fourthly, for DC mode, one combined predictor generation process is introduced and predictor generation of I16MB's DC mode is merged into the process of I4MB's DC mode. Moreover, by configuring proposed hardwired engines, predictor generation of I16MB's plane mode and chrominance plane mode can be accomplished with only 50% cycles of original design. Totally, when compared with original full-mode design and latest dynamic mode reused design, the proposed predictor generation engine can achieve 89.5% and 73.2% saving of processing cycles, respectively. Synthesized by TSMC 0.18 µm technology under worst work conditions (1.62 V, 125°C), with 380 MHz and 37.2 k gates, the proposed design can handle real-time high profile intra predictor generation of Super Hi-Vision 4 k4 k@60 fps. The maximum work frequency of our design under worst condition is 468 MHz.

This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.