A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.

Comparison of T-2 Toxin and HT-2 Toxin Distributed in the Skeletal System with That in Other Tissues of Rats by Acute Toxicity Test

Twelve healthy rats were divided into the T-2 toxin group receiving gavage of 1 mg/kg T-2 toxin and the control group receiving gavage of normal saline. Total relative concentrations of T-2 toxin and HT-2 toxin in the skeletal system（thighbone, knee joints, and costal cartilage） were significantly higher than those in the heart, liver, and kidneys（P 〈 0.05）. The relative concentrations of T-2 toxin and HT-2 toxin in the skeletal system（thighbone and costal cartilage） were also significantly higher than those in the heart, liver, and kidneys. The rats administered T-2 toxin showed rapid metabolism compared with that in rats administered HT-2 toxin, and the metabolic conversion rates in the different tissues were 68.20%-90.70%.

Distributed inverse synthetic aperture radar imaging of ship target with complex motion

For ship targets with complex motion,it is difficult for the traditional monostatic inverse synthetic aperture radar(ISAR)imaging to improve the cross-range resolution by increasing of accumulation time.In this paper,a distributed ISAR imaging algorithm is proposed to improve the cross-range resolution for the ship target.Multiple stations are used to observe the target in a short time,thereby the effect of incoherence caused by the complex motion of the ship can be reduced.The signal model of ship target with three-dimensional(3-D)rotation is constructed firstly.Then detailed analysis about the improvement of crossrange resolution is presented.Afterward,we propose the methods of parameters estimation to solve the problem of the overlap or gap,which will cause a loss of resolution and is necessary for subsequent processing.Besides,the compressed sensing(CS)method is applied to reconstruct the echoes with gaps.Finally,numerical simulations are presented to verify the effectiveness and the robustness of the proposed algorithm.

Meta-information generation in distributed information system

The authors discuss the concept of meta information which is the description of information system or its subsystems, and proposes algorithms for meta information generation. Meta information can be generated in parallel mode and network computation can be used to accelerate meta information generation. Most existing rough set methods assume information system to be centralized and cannot be applied directly in distributed information system. Data integration, which is costly, is necessary for such existing methods. However, meta information integration will eliminate the need of data integration in many cases, since many rough set operations can be done straightforward based on meta information, and many existing methods can be modified based on meta information.

Optimization in the Migration Problem of Mobile Agents in Distributed Information Retrieval Systems

In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.

Analysis of on-chip distributed interconnects based on Pade expansion

In this paper, on-chip interconnects are modeled as distributed parameter RLCG transmission lines, based on which the matrix ABCD of interconnects is deduced. With help of the ABCD matrix, a voltage transfer function of an interconnect system, consisting of a driver, interconnect line and load, is obtained analytically in the form of a transcendental function, and it is reduced to a finite order system based on high order Pade approximation. With the reduced-order transfer function, response waveforms with step input can be obtained, and signal delay can be calculated consequently. Two numerical experiments are conducted to demonstrate its efficiency.

Dynamic Multicast Grouping Approach in HLA-Based Distributed Interactive Simulation

In order to improve the efficiency of data distributed management service in distributed interactive simulation based on high level architecture (HLA) and to reduce the network traffic and save the system resource, the approaches of multicast grouping in HLA-based distributed interactive simulation are discussed. Then a new dynamic multicast grouping approach is proposed. This approach is based on the current publication and subscription region in the process of simulation. The results of simulation experiment show that this approach can significantly reduce the message overhead and use fewer multicast groups.

A Distributed In-Memory Database Solution for Mass Data Applications

In this paper, a Distributed In-Memory Database （DIMDB） system is proposed to improve processing efficiency in mass data applications. The system uses an enhanced language similar to Structured Query Language （SQL） with a key-value storage schema. The design goals of the DIMDB system is described and its system architecture is discussed. Operation flow and the enhanced SOL-like language are also discussed, and experimental results are used to test the validity of the system.

Alarm-Based Root Cause Analysis Based on Weighted Fault Propagation Topology for Distributed Information Network

A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted fault propagation graph.Different from other RCA methods,it mines effective features information related to root causes from offline alarms.Combined with the information,online alarms and graph relationship of network structure are used to construct a weighted graph.Thus,this approach does not require operational experience and can be widely applied in different distributed networks.The proposed method can be used in multiple fault location cases.The experiment results show the proposed approach achieves much better performance with 6%higher precision at least for root fault location,compared with three baseline methods.Besides,we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.

Application of Distributed Intelligent Power Supply Technology in Expressway Tunnels

With the coordinated development of today's social economy with science and technology,various advanced technologies are being used in highway engineering,especially the distributed intelligent power supply technology in expressway tunnels,which has a very significant advantage.In order to realize the effective application of this technology and promote the power supply effect in expressway tunnel,this study analyzes the advantages of this technology and its application in expressway tunnel,hoping to provide scientific reference for the application of distributed intelligent power supply technology and the engineering development of expressway tunnels.

Active Segment Pre-restoration Mechanism for Distributed Intelligent Optical Network

The development of Internet brings a great challenge to the survivability of the supporting distributed intelligent optical networks. The emergence of Active-Fault-Alarm (AFA) technology makes it possible for the system to be aware of the incoming or possible fault in advance and to provide possibility to develop a more active restoration mechanism. On this base, an Active Segment Pre-Restoration (ASPR) mechanism for distributed optical network is proposed. ASPR allows establishing a Segment Pre-Restoration Path (SPR-Path) as a work path, which is initiated by the local node, in advance of potential fault occuring and keeps the SPR-Path only during the low-quality or fault period. Simulation results show that the ASPR mechanism has better restoration performance compared with that of Active Restoration (AR) scheme.

Distributed In-Time Multiple Coverage Holes Healing Protocol

Coverage holes often appear in wireless sensor networks due to sensor failure or the inheritance of sensor＇s random distribution. In the hybrid model, mobile sensors in the network are acquired to heal coverage holes by their mobifity. When multiple coverage holes appear in the sensor network and each of them has a time requirement （in which the coverage hole has to be healed）, conflicts for the requests of the same mobile sensor may arise. A distributed multiple mobile sensor schedufing protocol （DMS） is proposed in this paper to solve this problem by finding mobile sensors in the time response zone defined by the time requirement of each coverage hole. Simulation results show that DMS can well schedule the mobile sensors to move to multiple coverage holes within the time requirement.

MA-IDS: A Distributed Intrusion Detection System Based on Data Mining

Aiming at the shortcomings in intrusion detection systems (IDSs) used incommercial and research fields, we propose the MA-IDS system, a distributed intrusion detectionsystem based on data mining. In this model, misuse intrusion detection system CM1DS) and anomalyintrusion de-lection system (AIDS) are combined. Data mining is applied to raise detectionperformance, and distributed mechanism is employed to increase the scalability and efficiency. Host-and network-based mining algorithms employ an improved. Bayes-ian decision theorem that suits forreal security environment to minimize the risks incurred by false decisions. We describe the overallarchitecture of the MA-IDS system, and discuss specific design and implementation issue.

e-Automation,an Architecture for Distributed Industrial Automation Systems

Due to the complexity of modern industrial systems, a conventional automation system is not capable of providing sufficient information management and high-level intelligent approaches, as achieving these functionalities requires the support of comprehensive data management and coordination between system devices and heterogenous information. This paper proposes the concept of e-Automation, in which computer networking and distributed intelligence agent technologies are applied to industrial automation systems, and presents a hardware and software architecture that implements this concept. An open infrastructure based on multi-agent systems is employed in the proposed architecture of e-Automation, which aims to allow the implementation of diverse tasks and to permit greater configurability than can be obtained from a traditional system. To evaluate our proposed e-Automation concept, this paper presents a case study of substation information management which adopts the proposed e-Automation architecture in power system domain.

Uniform wire segmentation algorithm of distributed interconnects

A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer inser-tion are obtained through computation and derivation, based on a 2-pole approximatian model of distribut-ed RLC interconnect. For typical inductance value and long wires under 180nm technology, experiments show that the uniform wire segmentation technique proposed in the paper can reduce delay by about 27%～56%, while requires 34%～69% less total buffer usage and thus 29% to 58% less power consump-tion. It is suitable for long RLC interconnect performance optimization.

A Metapopulation Model with Patches Distributed in Two Geographical Scales

We propose a metapopulation model with two geographical scales. In a regional scale, the model describes the dynamics of a collection of habitats connected by migratory movements. In a local scale, we consider some granularity within each habitat, in the sense that each habitat is itself a collection of patches linked by dispersal. The whole ensemble can be seen as a metapopulation composed by local metapopulations. We analyze the synchronization of the model in the two geographical scales. We present an analytic criterion for synchronization where only the habitats in the regional scale evolve with the same dynamics. Through numerical simulations, we discuss the different synchronization modes. It depends on how the individuals are distributed in the local patches that compose a habitat after migration takes place in the regional scale.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.