Network intrusion detection using fuzzy class association rule mining based on genetic network programming

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named genetic network programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.

Genetic network programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNPpsilas performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

In this paper, we propose a variant of Genetic Network Programming (GNP) which has multiple start nodes. GNP is a kind of evolutionary methods inspired from Genetic Programming (GP). While GP has tree architecture, GNP has a network architecture by which a single agent is controlled. In this paper, by increasing the number of start nodes in a network of GNP, we extend GNP to control multiple agents. We applied the extended GNP called Multi-Start Node GNP (MS-GNP) to a load transportation problem and a tile world problem.

Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

Genetic Network Programming (GNP) is a newly proposed evolutionary approach which can evolve itself and find the optimal solutions. It is a novel method based on the idea of Genetic Algorithm (GA) and uses the data structure of directed graphs. As GNP has been developed for dealing with problems in dynamic environments, many papers have demonstrated that GNP can be applied to many areas such as data mining, forecasting stock markets, elevator control systems, etc. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP with RI). In the proposed method, the worst individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worst individuals mimics the maturing phe nomenon in nature, where bad individuals can become smarter after receiving good education. GNP with RI has been applied to the the-world which is an excellent benchmark for evaluating the proposed architecture. The performance of GNP with RI is compared with conventional GNP demonstrating its superiority.

Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for dense databases. However, the conventional GNP data mining method can not find comparative relations and hidden patterns among a large amount of data. In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover comparative association rules between different datasets. GNP is an evolutionary approach recently developed, which can evolve itself and find the optimal solutions. The objective of the comparative association rules mining is to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute values of the confidence differences of the rules obtained from different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. On the other hand, the calculation is very time-consuming, when the conventional GNP based data mining is used for the large attributes case. So, we have proposed an attributes accumulation mechanism to improve the performances. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.

Genetic Network Programming(GNP) is extension of Genetic Algorithm(GA). The GNP is suitable for an agent programming. The GNP can evolve anoperation program of the agent. But, the GNP has the premature convergence problem as an evolution technique as well as GA. On the other hand, to avoid the initial convergence of the GA, Immune Alogorithm(IA) which is introduced the immune suppression to the GA had developed.Then if the GNP and IA are combined, a better algorithm for agent programming can be developed. Authors proposed Immune evolved Genetic Network Programming (IGNP). To compare the GNP and IGNP, the simmulation was done. As a result, IGNP is more exellent without the premature convergence than the GNP.In this paper, the authors explain the IGNP outline and a effectiveness of the IGNP is stated through the simulation result.

The intrusion detection aims at distinguishing the attack data and the normal data from the network pattern database. It is an indispensable part of the information security system. Due to the variety of network data behaviors and the rapid development of attack fashions, it is necessary to develop a fast machine-learning-based intrusion detection algorithm with high detection rates and low false-alarm rates. In this correspondence, we propose a novel fuzzy method with genetic for detecting intrusion data from the network database. Genetic algorithm is an evolutionary optimization technique, which uses Directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with a compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with Genetic proposes a new method that can deal with a mixed of database that contains both discrete and continuous attributes and also extract many important association rules to contribute and to enhance the Intrusion data detections ability. Therefore, the proposed method is flexible and can be applied for both misuse and anomaly detection in data-intrusion-detection problems. Also the incomplete database will include some of the missing data in some tuples and however, the proposed methods by applying some rules to extract these tuples. The Genetic-Fuzzy presents a data Intrusion Detection Systems for recovering data. It also include following steps in Genetic-Fuzzy rules: Process data model as a mathematical representation for Normal data.; Improving the process data model which improves the Model of normal data and it should represent the underlying truth of normal Data.; Uses cluster centers or centroids and use distances away from the centroids and convert the Data to Training Data. (8 pages)

Genetic Network Programming (GNP) is one of the evolutionary algorithms. It adopts a directed graph structure to represent a solution to a given problem. Agents judge situations and execute actions sequentially following the node transitions in the graph. On one hand, GNP possesses an advantage of node reusability, which makes it possible to realize a compact graph structure that represents a solution. On the other hand, the compact structure suggests that any connection might play a significant role in the solution, i.e., a slight change to the connections could tremendously influence the performance of the agents for the given task. The conventional GNP, however, lacks an effective way to evaluate and to take advantage of the connections. This paper thus proposes a reinforcement learning approach to learn GNP's subgraphs that contain a relatively small number of connections, and further proposes a partial reconstruction approach to modify the solution with the obtained subgraphs. These two approaches are combined together to form a new evolutionary learning model named GNP with Evolution-oriented Reinforcement Learning (GNP-ERL). Some experiments are conducted on the Tileworld testbed to verify the effectiveness of GNP-ERL, and the simulation results demonstrate that it outperforms the conventional GNP in both training and testing phases.

Genetic programming network (GNP), a graph-based evolutionary method, has been proposed as an extension of genetic algorithm (GA) and genetic programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with ant colony optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to elevator group supervisory control systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

A new evolutionary method named ” Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures which have some useful features inherently. For example, GNP has the implicit memory function which memorizes the past action sequences of agents, and GNP can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. In this paper, buying / selling model for stock market using GNP with Candlestick Chart has been proposed and its effectiveness is comfirmed by simulations.

A heterogeneous multi-agent system is a system that involves two or more agents that cooperate in order to accomplish a certain task. Genetic Network Programming (GNP) is a technique to automatically build a multi-agent system. In the past, the authors proposed the use of the Immune evolved Genetic Network Programming (IGNP) technique for the automatic construction of multi-agent systems. In this paper, the authors propose the use of Genetic Network Programming with Immune Adjustment Mechanism (GNPIAM) as a technique to automatically build a heterogeneous multi-agent system. In this study, the authors carry out experiments using tile world to evaluate the validity of the proposed method and compare the three techniques—GNP, IGNP, and GNPIAM.