22570791
A Novel Biology-Inspired Virus Detection Model with RVNS
A virus detection model based on artificial immune principles with real-valued negative selection (RVNS) is proposed. Feature vectors of program code are mapped into high dimension real-valued space. The architecture of this model, the formal definitions of self, nonself, antigen, antibody, and gene library are given. Then the process of self-tolerance of variable-sized detectors in real-valued space is discussed in detail. Variable-sized detectors provide concise representation of the detectors derived from benign files, which reduces the size of detectors set and advances the detection rate. The experimental results show that the model can detect obfuscated and firstly unknown virus more effectively than traditional model.
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.
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.
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.
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.
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.
27532025
Modeling and Prediction in the Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks
Artificial intelligence technique namely artificial neural network (ANN) was used to describe the enzymatic kinetics of cellulose hydrolysis in a heterogeneous system, and compared with response surface methodology (RSM). Three hydrolysis conditions (activity of added cellulase, substrate concentration and time) served as the input of the neural network model, and the glucose content served as the output. The experimental data from Box-Behnken design were used to train the neural network using the back propagation algorithm. The others of 33 design were used to check the performance of the trained network. The ANN modelled and predicted values showed better agreement with the experimentally reported ones than RSM. ANN could mimic the heterogeneous enzymatic hydrolysis of cellulose.
1131898
A Novel Spatial Autocorrelation Model of Shadow Fading in Urban Macro Environments
In this paper, we propose a novel spatial autocorrelation model of the shadow fading process in urban macro environments. The proposed model is based on the empirical results obtained from extensive wideband radio channel measurement campaigns at 2.35 GHz in an urban area of a typical medium-sized Chinese city. The shadow fading component was extracted assuming a single-slope log-distance path loss model. The consistency with the level crossing theory of Gaussian processes is achieved by an implicit constraint on the parameters of the model. The proposed model gives a better fit to the empirical results in individual measurement routes than the widely reported exponential and double exponential models. An heuristic explanation of the proposed autocorrelation property is also presented.
1600088
Automatic Acquisition of Context-Specific Lexical Paraphrases
Lexical paraphrasing aims at acquiring word-level paraphrases. It is critical for many Natural Language Processing (NLP) applications, such as Question Answering (QA), Information Extraction (IE), and Machine Translation (MT). Since the meaning and usage of a word can vary in distinct contexts, different paraphrases should be acquired according to the contexts. However, most of the existing researches focus on constructing paraphrase corpora, in which little contextual constraints for paraphrase application are imposed. This paper presents a method that automatically acquires context-specific lexical paraphrases. In this method, the obtained paraphrases of a word depend on the specific sentence the word occurs in. Two stages are included, i.e. candidate paraphrase extraction and paraphrase validation, both of which are mainly based on web mining. Evaluations are conducted on a news title corpus and the presented method is compared with a paraphrasing method that exploits a Chinese thesaurus of synonyms -- Tongyi Cilin (Extended) (CilinE for short). Results show that the f-measure of our method (0.4852) is significantly higher than that using CilinE (0.1127). In addition, over 85% of the correct paraphrases derived by our method cannot be found in CilinE, which suggests that our method is effective in acquiring out-of-thesaurus paraphrases.
1495082
A coordinate SPC model for assuring designated fit quality via quality-oriented statistical tolerancing
On the basis of studies to process quality indices and fit quality indices, the quality-oriented statistical tolerancing is introduced, which provides an interface between fit quality and the process parameter design of mating parts. A statistical mating model via the quality-oriented statistical tolerancing under information-based manufacturing condition is discussed. Compared with traditional mating model for a pair of hole/shaft fit, the statistical mating model is developed for equal batches of hole/shaft fit, and it can achieve a designated fit quality and minimum defective ratio by fully utilizing statistical tolerance zone and the known information of manufactured mating parts via a coordinate SPC model and local network and information system. Thus, production cost for mating parts can be reduced obviously. This new approach provides a possibility and guideline to concurrent design of the process quality, statistical tolerances to assure designated process quality and fit quality for hole and shaft parts.
18794722
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR improve ultimately in many ways.