In view of the fact that the distributed code behavior vulnerability fuzzy scanning algorithm does not consider the vulnerability feature selection method, which leads to the low accuracy and long scanning time of dis...
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In view of the fact that the distributed code behavior vulnerability fuzzy scanning algorithm does not consider the vulnerability feature selection method, which leads to the low accuracy and long scanning time of distributed code behavior vulnerability fuzzy scanning. A distributed code behavior vulnerability fuzzy scanning algorithm based on machine learning is proposed. According to the characteristics of kernel principal component analysis, this paper compares the effects of different kernel functions, obtains the vulnerability feature selection method and kernel function, finds the most suitable feature mapping, and transforms it into a feature sample set. In depth study of vulnerability scanning technology, combined with the characteristics of P2P network structure and vulnerability scanning, the distributed code behavior vulnerability fuzzy scanning model is constructed. Each scanning node cooperates with each other to complete the scanning task issued by the user, and the node join and exit mechanism are designed to seek the optimal scheduling matrix of the scanning task set, complete the vulnerability scanning task scheduling, and realize the distributed code line fuzzy scan for vulnerabilities. Experimental results show that the proposed algorithm has high accuracy and can effectively shorten the vulnerability scanning time.
In this paper, a Model Driven Architecture (MDA) approach is applied to Semi-automatically translate sequential programs into corresponding distributed code. The novelty of our work is the use of MDA in the process of...
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ISBN:
(纸本)9780769537474
In this paper, a Model Driven Architecture (MDA) approach is applied to Semi-automatically translate sequential programs into corresponding distributed code. The novelty of our work is the use of MDA in the process of translating serial into distributed code. The transformation comprises automatic generation of platform independent and then platform specific models from the sequential code. In order to generate the PIM, a meta-model defining the overall architecture of the resultant distributed code is developed. The meta-model is used as a basis for the development of platform independent models (PIM) for the resultant distributed code. A set of transformation rules are defined to transform the resulted PIM into a corresponding platform-specific model. These transformation rules can be modified by the user, depending on the details of the underlying middle-ware applied for the distribution. The platform independent model provides a better understanding of the distributed code and helps the programmer to modify the code more easily.
The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns acro...
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The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field of arbitrarity many nodes, whose activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse, whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three synaptic transmission functions, a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all. When source field activity is distributed, catastrophic forgetting may occur Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the unit of long-term memory in such a system is an adaptive threshold, rather than the multiplicative path weight widely used in neural models.
Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving ...
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ISBN:
(纸本)9789048191123;9789048191116
Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.
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