Recently, security issues have become more serious with the spread of IoT devices. To reduce the security risks when using communication devices, network administrators should know detailed information of all devices connected in the network and control them accordingly. Although there are many studies about identifying devices based on information from the network, there is a limit to improving the accuracy of device identification by using a single identification method.In this paper, we propose a new method to improve the accuracy of device identification by using a combination of results from multiple identification methods. We define a new index, called similarity, between multiple devices by using the results from multiple identification methods and identify devices based on the similarity. The results of evaluations for communication devices connected to a real network show that our method can improve the accuracy of device identification by 23.0 percent.

An exact analysis is presented for bit error probability of Max-Log-MAP decoding in sequence dependent channels. In the channels, an average bit error probability is derived by averaging the bit error probability of fixed sequence over all sequences, however, it causes the exponential complexity increase with a sequence length.In this paper, a new method is proposed for the averaging operation in the exact error probability analysis of Max-Log-MAP decoding. It is based on the integration of the probability density function (pdf's) of merging path metrics at every trellis transition. An illustrative example of bit error probability is shown for 2-state intersymbol interference channels.

Recently, network traffic is becoming strongly biased by user's action taken in an application. In this paper, we propose a method to infer such action (we define as “user behavior”) from the monitored traffic. The proposed method firstly composes a set of traffic features (statistical features of measured traffic flows) and then applies a Supervised Machine Learning (ML) algorithm to identify the user behavior from the statistical features. Through experimental results by using actual traffic, we show that the proposed method achieves around 91% accuracy of identification for 9 major applications, and around 81% accuracy of identification for 43 user behaviors.

Recently, variety of communication devices such as printers, IP telephones, network cameras are used widely, with the support of networking in consumer electronics. As a spread of IoT (Internet of Things), the number of embed devices are significantly increasing, however, such devices have lack of capability on security. It is therefore desirable that a network identifies these devices to take appropriate operations. In this paper, we propose an identification method of communication devices from monitoring patterns of traffic, here we use statistical metrics such as packet inter-arrival time or packet size, and we apply a machine learning for the identification. Through evaluations using real traffic, we show that our method can achieve over 90% of identification to 9 communication devices.

Packet classification has become increasingly complex and important to network equipment intended for future use. A recent trend to achieve complex packet classification is to use software-based methods, which tend to be slower than hardware-based methods. For search, this typically means using ternary content-addressable memory (TCAM) to make classification feasible. However, TCAM is not well-suited to the long (in bits) and sparse rules used for running advanced applications that require complicated classification. We propose a multi-dimension search engine (MDSE) that is optimized for use with long, sparse rules, and we propose a multi-dimensional TCAM scheme, which is an MDSE constructed to operate on TCAM. Through fine-grained simulations with real traffic, we show that our proposed search engine can reduce the power consumed by network equipment by about 85%.

Software-defined Networking (SDN) is a new network architecture that decouples the control plane from the data plane. Scalability of the control plane with respect to network size and update frequency is an important problem that has been addressed by previous studies from a variety of viewpoints. However, the solutions found in these studies may be only locally optimized solutions. To find a globally optimized solution, a broader viewpoint is required: one in which various SDN architectures can be evaluated and compared. In this paper, we propose an abstract model of SDN architectures, which enables multiple SDN architectures to be compared under a unified evaluation condition, and discuss the modeling of SDN architecture and its variations to find the optimal design from a global viewpoint. We first propose a generic model of SDN architectures and derive variations in terms of composition unit (single or multiple), processing principle (sequential or parallel), or location (intra- or inter-node). We then show that existing SDN architectures can be represented as one of the variations of our abstract model with fitted parameters. Finally we discuss how variation of components affects performance and show, using message-driven simulations, that our model enables comprehensive performance comparisons of different SDN designs represented as parameterized models.

It is important to use cache efficiently for the content deployment in the aspect of reducing load of server and latency, especially in content-oriented networks such as ICN (Information Centric Networking). Since the capacity of cache on each network node is limited, numbers of cache replacement algorithm have been proposed. However, because of previous methods do not consider the rapid fluctuation of content demand, ineffectual cache of content remains which cause a waste usage of cache capacity. In this paper, we propose the method of using the prediction result of content demand. We first demonstrate that by applying prediction can improve the efficiency of cache utilization. However, predicting the demand of whole contents is not realistic in the aspect of computational cost. We therefore propose the method of reducing computational cost greatly without lack of cache efficiency. We demonstrate that by simulating with real monitored data, cache hit rate can be increased about 1.6 times. Furthermore, we show that our method has advantage in many aspects, such as efficient usage of cache and content deployment based on its popularity in the whole network by comparing with other existing methods.

Software-Defined Networking (SDN) is a new approach to manage the whole network flexibly by decoupling the control plane and the forwarding plane. While forwarding elements can be managed by a unified control, complexity arisen from the network size and scalability regarding the increase of the control traffic are notable problems. To deal with events of network reconfiguration that occur asynchronously and change frequently with intervals shorter than hours, a controller has to continue to asynchronously update the configuration of the whole network. However, it is hard to maintain the consistency of the configuration of the whole network because it needs to manage a huge amount of network information and to deal with user requests that occur asynchronously. In this paper, we propose a database oriented management for asynchronous reconfiguration to achieve the consistency of configuration in SDN. We design a structure of the database to store network information and two functional components. Finally, we adopt our management system to an OpenFlow-based network, and validate that our system can manage and control an OpenFlow network via the database.

In the binary tree network composed of a binary symmetric channel (BSC), the bit error probability of majority decision at the fusion center is found to be almost constant regardless of a network size. In this paper, we consider a fading channel for BSC in the binary tree network. In the fading channel, the error probability becomes a random variable. 2nd-order moments of error probability are derived by an iterative calculation of correlation matrix. Numerical results show that the mean and variance of error probability does not affected by the network size and keeps constant.

Recently, energy consumption of routers has become a serious problem, hence power reduction is an urgent and important challenge. Existing routers always work 100% of their potentials regardless of required performance, such as volume of input traffic. However, semiconductor devices such as lookup logics, buffers, fabrics are not always fully utilized. In particular, the occupancy of packet buffer is very low in many cases, especially in core routers, which leads to unnecessary consumption of electric power. To solve this problem, a new buffer architecture called Sliced Packet Buffer was proposed. Dividing a whole buffer into multiple sub buffers (called slices) at the LSI level enables to control power management independently according to its occupancy. However, input traffic rate is another parameter for further power savings, which was not considered so far. In this paper, we propose a Two Dimensional Sliced Packet Buffer which enables power management according to not only the buffer occupancy but also the traffic volume. We also propose a model of accurate performance evaluation on energy consumption in the granularity of operational instruction. Through trace-driven simulations with real traffic, we show that our proposed packet buffer can reduce an average 66% of power consumption when an average input rate is 30%.

Today, online social networking (OSN) has become the most used application service in the Internet. As shown in Facebook and Twitter, the network is constructed based on the social community structure, and the information is propagated from friend to friend, like a word of mouth. Such social-centric networking is one of new directions for new communication style in the Future Internet, which realizes scalable, robust, and self-controllable traffic control. However, many technical challenges exist to apply the structure of social network to the real networking infrastructure, especially vulnerability or workload of hub nodes. This paper first states the overworked hub problem which degrades seriously the search efficiency in social-centric networks. We propose a new communication style to solve the problem by using cross-community approach. Through preliminary results we show that workload of hub node can be reduced and information retrieval efficiency is improved by considering cross-community approach.

For the performance evaluation of convolutional codes, computer simulations are widely used, and few studies have been made for exact error probability of convolutional codes of more than 4-state. In this paper, an analytical approach is presented for moments of decision variables for a maximum a-posteriori probability (MAP) decoding. The moments are derived by recurrence relations. An application of the moments is demonstrated for error probability. In order to see a fast convergence in the moment techniques, a modified MAP is employed. The bit error probability of the modified MAP decoding is expressed by both the Gram-Charlier expansion and the maximum entropy method and the validity of the moments derivation has been confirmed.

A severe spectrum shortage for wireless communication systems poses the spectrum overlapping and the interference problem with a significant performance degradation. To avoid this problem, it is essential to employ the techniques for interference mitigation. The goal of this paper is to investigate the performance of some interference mitigation techniques in the systems using Viterbi decoding, i.e., 1-norm metric, received power based limiting, puncturing and metric based limiting. These methods are compared from the view point of bit error rate (BER) obtained by computer simulations. Simulation results show the superiority of metric based limiting, which yields better BER performance.

In adaptive modulation systems, the receiver should identify the modulation employed at the transmitter. This paper presents a new modulation classification method based on amplitude moments and correlation between subcarriers in Orthogonal Frequency Division Multiplexing (OFDM) systems. In the OFDM systems, a frequency offset causes inter-carrier interference (ICI), and the amplitude moments of received signal include ICI components. The proposed method estimates and removes the ICI components using the correlation between subcarriers. Simulation results of classification error probability show that the proposed method is robust to frequency offset.

When multiple flows using different versions of Transmission Control Protocol (TCP) exist together on a network, they seriously affect each other. One method to overcome this problem is to control such flows individually by identifying the TCP versions of each flow. Thus, for this process, it is necessary to identify online which TCP version is used in each flow. In this paper, we first focus on a method for controlling the congestion window (cwnd) in different versions of TCP. We find out characteristic values using this method and then propose a method to identify flow by using the characteristics. We show that any 2 out of 14 TCP versions that are implemented in Linux can be identified by using our proposed method.

Recent studies have described the topologies of various networks are categorized as scale-free networks, focusing especially on the scale-free nature of the Internet. However, the suitability of the current topology of the Internet for communications has not been studied. In this paper, we investigate whether the current Internet is optimized on both aspects of communication efficiency and attack tolerance. For this purpose, we define four metrics to represent the capabilities of the network from various viewpoints. Our investigation showed that there is a network topology superior to that of the current Internet. We then considered how we should change the network topology from the current to more appropriate one. We introduce four typical strategies that the end-user can act and evaluate which strategy is most effective to re-organize the topology of the Internet to the new one.

An end-to-end round trip time (RTT) is one of the most important communication characteristics for Internet applications. From the viewpoint of network operators, RTT may also become one of the important metrics to understand the network conditions. For this case, we should know how a factor such as network incident influences RTTs. It is obvious that two or more factors may interfere in observed delay characteristics, because packet transmission delays in the Internet are strongly dependent on the time-variant condition of the network. From this reason we previously proposed a modeling method by using mixed distribution which enables to express delay characteristic more accurately where two or more factors which affect network delays exist together. However, this is not clear that how the factors affect the results of measured RTTs, and no investigations have been shown to infer the network behavior based on modeling results. In this paper, we propose an estimation method of network behavior by decomposition of the mixed distribution. Furthermore, in experimental environment, we investigate the influence caused by each network impact factor independently. By applying the proposed method, we also presume the events that occur in network from the measurements of RTTs by using the results of decomposition.

In this letter, a general orthogonal-modulation model to develop new modulations and to identify some widely used modulation schemes is proposed. Based on this general framework, an optimum scheme for non-Gaussian channels under a given set of constraints can be derived. In software radios, the modulation should be changed by tracking this optimum scheme. The proposed model is based on orthonormal vectors, which are obtained by multidimensional rotations. Several widely used modulation schemes are renamed by their consecutive multidimensional rotation angles within the proposed framework. New modulation examples in continuous-wave interference and impulse interference channels are given to illustrate the corresponding local optima.

For distributed file transfers, parallel downloading is one of effective schemes to shorten a transmission time and/or to get more file transfer rate. In parallel downloading, single file is transferred by using multiple connections to different file servers. However, the effectiveness of parallel downloading is strongly dependent on the physical network topology, i.e., if some connections share the same bottleneck link, the throughput is not increased and the additional connection simply wastes the server resources. In this paper, we propose a server selection method for parallel downloading by taking the physical network topology into consideration. Our approach tries to minimize the total number of shared links between a client and downloading servers. Through simulation results, we show that our method can keep high throughput regardless the network topology and decrease the number of selected servers compared with random server selection.

In recent years, high-performance routers are needed as well as increasing the link capacity to keep up with the Internet traffic explosion. Routers are preferable to store statistics of the incoming/outgoing traffic to realize the intelligent or policy-based services in the Internet. In this paper, we propose a new packet classification solution, which uses the combination of CAM and DRAM memories. Unlike other algorithms, we do not search the classifier by selecting the front bits, but select bits arbitrary from the classifier as the search key in the CAM. For this purpose, we propose new bit selection methods. Through simulation results, we show that our bit selection methods can decrease the delay of classification.

In this paper, we demonstrate an exact analysis of bit probability for trellis coded modulation by using an analytical technique which is effective even in the region of low signal-to-noise power ratio (SNR). This employs an iterative calculation of probability density function (pdf) of path metric per transition, and is applicable for multilevel coded modulation. We show error performance results of 4-ary trellis coded modulation employing 2-state convolutional encoders.

A global message communication network for low-density traffic, using satellites at low altitudes, is described. This network affords around-the-clock service to any part of the globe, including the polar regions. Such a network can accommodate static and mobile user terminals simultaneously. The oblate globe is modeled as a regular polyhedron with 12 facets for setting up the orbits. Satellites are positioned uniformly in low-altitude symmetrical orbits. The symmetrical orbits are those whose axes are symmetrical in the three-dimensional space. The authors study the coverage aspects of the 6-orbit scheme and the 10-orbit scheme, each with satellites deployed at an altitude of 5000 km. At this altitude, the terrestrial user terminals can access satellites at a grazing angle of 45 degrees . The method of access over the crosslinks is the slotted ALOHA scheme. For low-density traffic, the downlink and uplink throughput rates are estimated. Simulation results agree with these analytical estimates for low values of network offered load.>

The computation of upper and lower bounds to error probability in digital transmission over nonlinear channels with a finite memory is considered. By using orthogonal Volterra series, the authors derive a canonical representation for discrete nonlinear systems, based on a linear convolutional code and a memoryless mapper. This representation shows that finite-memory, discrete nonlinear systems can be analyzed in much the same way as TCM (trellis-coded modulation) schemes. In particular, TCM over nonlinear channels can be analyzed. A technique is derived that expresses an upper bound to error probability based on the computation of the transfer of a state diagram with N branches, and whose branch labels are matrices rather than scalars. Some examples of its application are given. In particular, error bounds are derived for nonlinear TCM schemes and for TCM schemes operating on nonlinear channels.>

This paper presents cochannel interference cancelling techniques in on-board processing satellite systems using orthogonal polarizations. The techniques, based on mean square error (MSE) criterion and convolutional coding/soft decision Viterbi decoding (SDVD), are introduced on board. The bit error rate (BER) is derived for the mixed detection of a desired signal and an undesired cochannel interference when the techniques are applied to binary phase shift keying (BPSK), and is compared to that of no compensation for the interference. Performance results, obtained theoretically, show that the techniques of both the MSE and SDVD processings realize excellent improvements of the BER, and the improvements become significant in a large interference environment.