43302296
A Novel Immune Based Approach for Detection of Windows PE Virus
Generic computer virus detection is the absolute need of the hour as most commercial antivirus products fail to detect unknown and new Windows PE viruses. Motivated by the success of immune-based techniques in intrusion detection systems, recent research in detecting computer viruses is directed towards devising efficient non-signature-based techniques. We observe that each Windows PE virus whether or not it is encrypted must have a relocation module to relocate its variables or constants in the infected programs. Due to its unique characteristic, the virus relocation module can be extracted as an antibody in the immune systems to detect the specific antigens. In this paper, we presented a novel Windows PE virus detection approach that draws inspiration from artificial immune system and the structure of the relocation module of the virus. The structure of Windows PE virus is sufficiently analyzed. The dynamic evolution of self and nonself, the presentation of the antigen, and the generation of the antibody are proposed. The experiment is conducted and its results indicate that this approach not only has relatively higher detection rate of unknown Windows PE virus than the earlier known methods, but also has better capability of self-adaptive and self-learning.
18262600
Parameters optimization of support vector regression based on immune particle swarm optimization algorithm
A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
14272122
A Dynamic Immunity-Based Model for Computer Virus Detection
Internet provides a fertile medium for new breeds of computer viruses. Many people who have access to a wealth of information via Internet are attacked by more computer viruses than they can effectively process. We present a dynamic computer virus detection model that can detect known viruses and previously unknown viruses to prevent information systems from damage. This model is inspired by biological immune systems that protect the body against damage from pathogens. The architecture of this model, the formal definitions of self, nonself, antigen, antibody, and vaccine gene library are introduced. Furthermore, the evolution of self and nonself, the generation of the antibody, the evolution of the virus vaccine gene, and the detection of the antigen are depicted. Experiment results show that this model has better capacity of self-adaptability and self-learning in detecting unknown viruses than traditional models.
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.
10021622
Dynamically Maintaining Duplicate-Insensitive and Time-Decayed Sum Using Time-Decaying Bloom Filter
The duplicate-insensitive and time-decayed sum of an arbitrary subset in a stream is an important aggregation for various analyses in many distributed stream scenarios. In general, precisely providing this sum in an unbounded and high-rate stream is infeasible. Therefore, we target at this problem and introduce a sketch, namely, time-decaying Bloom Filter (TDBF). The TDBF can detect duplicates in a stream and meanwhile dynamically maintain decayed-weight of all distinct elements in the stream according to a user-specified decay function. For a query for the current decayed sum of a subset in the stream, TDBF provides an effective estimation. In our theoretical analysis, a provably approximate guarantee has been given for the error of the estimation. In addition, the experimental results on synthetic stream validate our theoretical analysis.
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.
27685496
An Open Framework for Remote Electronic Elections
We propose a framework for remote electronic elections with an independent, trustworthy authorization proxy. Unlike existing voting systems, voter authorization is separated from particular elections in our scheme, and is done through reusable credentials granted by the proxy. Moreover, different types of elections can fit in the framework, with different sets of legitimate voters and even different designs of voting and tabulation. We also define a cryptographic protocol for the credential generation and the election registration.
39739560
Optimal Motion Generation of Flexible Macro-micro Manipulator Systems Using Estimation of Distribution Algorithm
In this paper, a new approach for motion generation and optimization of the flexible macro-micro manipulator system is proposed based on Estimation of Distribution Algorithm (EDA). The macro-micro manipulator system is a redundant system, of which inverse kinematics remains challenging, with no generic solution to date. Here, the manipulator system configurations, or the optimal joint motions, are generated using the EDA algorithm base on Gaussian probability model. Compared with simple genetic algorithms (SGA), this approach uses fewer parameters and the time for motion optimization is remarkably reduced. The proposed approach shows excellent performance on motion generation and optimization of a flexible macro-micro manipulator system, as demonstrated by the simulation results.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.
36214906
New routing algorithms in trustworthy Internet
Since the lack of source IP address checking in today's Internet makes it easy for the attackers to spoof the source address, the mechanism of Source Address Validation Architecture (SAVA) is proposed for building effective source-address validation to ensure that packets forwarded hold authentic source addresses. In this paper, we study the routing selection in partial deployment of SAVA nodes in the trustworthy Internet. We propose and compare three routing algorithms, i.e., Shortest-Path Algorithm (SPA), SAVA based Shortest-Path Algorithm (SSPA), and SAVA based Shortest-Path Algorithm with Load Balancing (SSPALB). Beside analytical results, we run simulations which met our expectations.
41660705
Fault Diagnosis on Satellite Attitude Control with Dynamic Neural Network
With increasing demands on higher performance, more safety and reliability of dynamic systems, especially on safety-critical systems, fault diagnosis became a research interests in recent years. In this paper, a systematic approach to design fault diagnosis and accommodation with compound approach such as applying improved robust observer to fault diagnosis on linearized system aided by dynamic neural network, which is trained to bridge the gap between simulated system and real system on nonlinear attributes and modeling errors. Using an instance of fault diagnosis on attitude control system of satellite attitude, advantages of new scheme are tested to be effective.