DESNN Algorithm For Communication Network Intrusion Detection

Intrusion detection is a crucial technology in the communication network security field. In this paper, a dynamic evolutionary sparse neural network (DESNN) is proposed for intrusion detection, named as DESNN algorithm. Firstly, an ensemble neural network model is constructed, which is processed by a dynamic pruning rule and further divided into advantage subnetworks and disadvantage subnetworks. The dynamic pruning rule can effectively reduce the subnetworks weight parameters, thereby increasing the speed of the subnetworks intrusion detection. Then considering the subnetworks performance loss caused by the dynamic pruning rule, a novel evolutionary mechanism is proposed to optimize the training process of the disadvantage subnetworks. The weight of the disadvantage subnetworks approach the weight of the advantage subnetworks by the evolutionary mechanism, such that the performance of the ensemble neural network can be improved. Finally, an optimal subnetwork is selected from the ensemble neural network, which is used to detect multiple types of intrusion. Experiments show that the proposed DESNN algorithm improves intrusion detection speed without causing significant performance loss compare with other fully-connected neural network models.

Efficient Breadth-First Reduct Search

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

Conflict Directed Clause Learning for Maximum Weighted Clique Problem

The maximum clique and minimum vertex cover problems are among Karp's 21 NP-complete problems, and have numerous applications: in combinatorial auctions, for computing phylogenetic trees, to predict the structure of proteins, to analyse social networks, and so forth. Currently, the best complete methods are branch & bound algorithms and rely largely on graph colouring to compute a bound. We introduce a new approach based on SAT and on the "Conflict-Driven Clause Learning" (CDCL) algorithm. We propose an efficient implementation of Babel's bound and pruning rule, as well as a novel dominance rule. Moreover, we show how to compute concise explanations for this inference. Our experimental results show that this approach is competitive and often outperforms the state of the art for finding cliques of maximum weight.

A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.