35751769
CNP: A Protocol for Reducing Maintenance Cost of Structured P2P
With highly dynamic, structured P2P system needs very high maintenance cost. In this paper we propose a Clone Node Protocol to reduce the maintenance cost of structured P2P system by a mechanism of clone nodes. In order to verify the efficiency of CNP, we achieve a Clone Node Chord structure based on CNP, i.e. CNChord. Furthermore, we implement a bidirectional CNChord (BCNChord) in order to reduce query time of CNChord. Theoretical analysis and experimental results show that CNChord can greatly reduce the cost of maintaining P2P structure and BCNChord can effectively improve the query speed. In a word, CNP can effectively reduce the maintenance cost of structured P2P.
33934028
An FSM-Based Approach for Malicious Code Detection Using the Self-Relocation Gene
Most information attacks on the Internet are perpetrated by deploying malicious codes, which results in destructive epidemics. The correct execution of viruses and worms throughout the Internet is accomplished by self-relocation. Self-relocation is a built-in mechanism in most malicious codes, allowing them to get the base address of the host program to correctly infect it. Since most legitimate computer programs do not need self-relocate themselves, the detection of malicious codes could be reduced to the detection of the various mutations of the self-relocation gene. This study presents such a detection mechanism based on finite state machine theory for both known and previously unknown malicious executable codes. It does not rely on signature-screening of known viruses, but instead it detects self-relocation attempt of the suspicious executable code. The experiments were conducted and the results indicate that the proposed approach has better ability to detect known and previously unknown malicious executable codes than other methods.
19419085
Computer Virus Evolution Model Inspired by Biological DNA
There has been considerable interest in computer viruses since they appeared. Firstly, computer viruses make software developers pay more attention to security and develop anti-virus technology more carefully. Secondly, computer viruses are vital simulation platforms to study biological viruses. Moreover, computer viruses (a form of artificial life) are helpful to enhance the understanding of the philosophy of life. Therefore, computer viruses and their evolutions are worth thoroughly studying in scientific research sense. To further investigate the evolution of computer viruses, a DNA-based model for computer viruses evolution, which draws inspiration from biological DNA and artificial life, is proposed. The formal definition of computer virus is introduced, and the evolution operators which include selection, mutation, and recombination operator are presented. The simulation experiments were conducted and the results indicate that computer viruses have enormous capabilities of self-propagation and self-evolution.
29802848
Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows
Detecting duplicates in data streams is an important problem that has a wide range of applications. In general, precisely detecting duplicates in an unbounded data stream is not feasible in most streaming scenarios, and, on the other hand, the elements in data streams are always time sensitive. These make it particular significant approximately detecting duplicates among newly arrived elements of a data stream within a fixed time frame. In this paper, we present a novel data structure, Decaying Bloom Filter (DBF), as an extension of the Counting Bloom Filter, that effectively removes stale elements as new elements continuously arrive over sliding windows. On the DBF basis we present an efficient algorithm to approximately detect duplicates over sliding windows. Our algorithm may produce false positive errors, but not false negative errors as in many previous results. We analyze the time complexity and detection accuracy, and give a tight upper bound of false positive rate. For a given space G bits and sliding window size W, our algorithm has an amortized time complexity of $ O + (\sqrt{G/W}) $. Both analytical and experimental results on synthetic data demonstrate that our algorithm is superior in both execution time and detection accuracy to the previous results.
17718771
Balancing Trust and Incentive in Peer-to-Peer Collaborative System
In order to maximize resource utilization as well as providing trust management in P2P computing environments, we propose a novel framework - Trust-Incentive Service Management (TIM). Having child model, club model, bid model and trust model, TIM is constructed based on role based price strategy. In this mechanism, providers set the price according to demand and supply, and consumers maximize the surplus upon budget and deadline. A weighted voting scheme is also proposed to secure the P2P system by declining the join request from malicious peers. TIM is scalable and efficient in that: (1) it is completely distributed without a central server; (2) it dynamically manages the price and service by integrating valuables of pricing, trust, and incentive. A TIM prototype has been successfully implemented in a real P2P system, CROWN. We evaluate the proposed approach through comprehensive experiments and achieve improved results in service allocation efficiency, system completion time, and aggregated resource utilization.
17429523
A Novel Trust-based Cooperative Node Selection Scheme in Wireless Sensor Networks
For communication between the cluster head node (CHN) and the distant data fusion center (DFC) with the cooperation of intra-cluster nodes in wireless sensor networks (WSNs), reliable handling of packets by the cooperative node (CN) is difficult to ensure. In this paper, we propose a novel CN selection scheme based on trust for WSNs. This scheme can choose one of high trust nodes to assist in the transmission of CHN, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. First, we establish a trust model to improve the reliability of transmission by CN in WSNs, which is based on both the direct trust and the indirect trust of nodes. Then the CHN selects a node based on the proposed trust model to aid the CHN in transmission. Simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
18825124
A Trajectory Clustering Algorithm Based on Symmetric Neighborhood
Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not discover small and dense clusters and be sensitive to two input parameters. Based on the partition-and-group framework, we propose a simple but effective trajectory clustering algorithm based on symmetric neighborhood named BSNTC, which needs only one input parameter which eases the sensitivity of parameters in a certain extent. The proposed measure considers both neighbors and reverse neighbors of trajectories when estimating its density distribution. Also, we use an accumulator without calculating influence outlier of each trajectory to reduce the computation cost and corresponding storage cost. A comprehensive performance evaluation and analysis shows that our method is not only efficient in the computation but also effective in arbitrary shape and different densities trajectory databases.
17359946
Optimized Cross-Layer Design for Scalable Video Transmission Over the IEEE 802.11e Networks
A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design.