Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 857-861
Author(s):  
Hui Ling Guo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Fitness Function ◽  
Adaptive Method ◽  
Rule Base ◽  
Rule Generation ◽  
Extraction Technology ◽  
Audit Data ◽  
Network Intrusions

It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

Research and Implementation of Intrusion Detection Based Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 946-951 ◽  
Author(s):  
Guang Cai Cui ◽  
Bai Tong Liu
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Real Time ◽  
Original Data ◽  
Network Data ◽  
Initial Population ◽  
Classification Rules ◽  
Detection Technology ◽  
Network Intrusions ◽  
Data Source

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

Research and Implementation of Professional Quality of Personnel Evaluation System Based on Multi-Dimensional

2014 ◽  
Vol 687-691 ◽  
pp. 4725-4729
Author(s):  
Min Jiang ◽  
Ying Jiang
Keyword(s):  
Genetic Algorithm ◽  
Evaluation System ◽  
Fitness Function ◽  
Paper Test ◽  
Simulation Experiments ◽  
Automatic Group ◽  
Paper Making ◽  
Professional Quality ◽  
Speed Up

this paper uses a new genetic algorithm (New Genetic Algorithm, NGA) to implement the automatic group volume function, solving the problem that the paper is not fully considered scores distribution of knowledge points in the test paper (test dimension) in the system using traditional genetic algorithm (Genetic Algorithm, GA) to implement automatic group volume, putting forward and redefining the fitness function to speed up the convergence. Simulation experiments show that the NGA algorithm is not only efficient, but can generate a valid paper, making the paper score can on the multi-dimensional of the test as far as possible to achieve uniform distribution.

OntoQuery-An Evolutionary Approach for Query Formation and Information Retrieval

2011 ◽  
pp. 140-160
Author(s):  
Sheng-Uei Guan ◽  
Chang Ching Chng ◽  
Fangming Zhu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Relevance Feedback ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Retrieval Scheme

This chapter proposes the establishment of OntoQuery in an m-commerce agent framework. OntoQuery represents a new query formation approach that combines the usage of ontology and keywords. This approach takes advantage of the tree pathway structure in ontology to form queries visually and efficiently. Also, it uses keywords to complete the query formation process more efficiently. Present query optimization techniques like relevance feedback use expensive iterations. The proposed information retrieval scheme focuses on using genetic algorithms to improve computational effectiveness. Mutations are done on queries formed in the earlier part by replacing terms with synonyms. Query optimization techniques used include query restructuring by logical terms and numerical constraints replacement. Also, the fitness function of the genetic algorithm is defined by three elements, number of documents retrieved, quality of documents, and correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query.

scholarly journals A design of fuzzy inference systems to predict tensile properties of as-cast alloy

2021 ◽  
Author(s):  
He Tan ◽  
Vladimir Tarasov ◽  
Anders E. W. Jarfors ◽  
Salem Seifeddine
Keyword(s):  
Genetic Algorithm ◽  
Tensile Properties ◽  
Tensile Testing ◽  
Fuzzy Inference ◽  
Cast Alloy ◽  
Rule Base ◽  
Fuzzy Inference Systems ◽  
Deterministic Models ◽  
Inference Systems

AbstractIn this study, a design of Mamdani type fuzzy inference systems is presented to predict tensile properties of as-cast alloy. To improve manufacturing of light weight cast components, understanding of mechanical properties of cast components under load is important. The ability of deterministic models to predict the performance of a cast component is limited due to the uncertainty and imprecision in casting data. Mamdani type fuzzy inference systems are introduced as a promising solution. Compared to other artificial intelligence approaches, Mandani type fuzzy models allow for a better result interpretation. The fuzzy inference systems were designed from data and experts’ knowledge and optimized using a genetic algorithm. The experts’ knowledge was used to set up the values for the inference engine and initial values for the database parameters. The rule base was automatically generated from the data which were collected from casting and tensile testing experiments. A genetic algorithm with real-valued coding was used to optimize the database parameters. The quality of the constructed systems was evaluated by comparing predicted and actual tensile properties, including yield strength, Y.modulus, and ultimate tensile strength, of as-case alloy from two series of casting and tensile testing experimental data. The obtained results showed that the quality of the systems has satisfactory accuracy and is similar to or better than several machine learning methods. The evaluation results also demonstrated good reliability and stability of the approach.

scholarly journals Optimising the time-cost-quality (TCQ) trade-off in offshore wind farm project management with a genetic algorithm (GA)

2020 ◽  
Vol 27 (1) ◽  
pp. 1-12
Author(s):  
Gloria Yushan Liu ◽  
Eric Wai Ming Lee ◽  
Richard Kwok Kit Yuen
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Models ◽  
Wind Farm ◽  
Fitness Function ◽  
Offshore Wind ◽  
Time Cost ◽  
Offshore Wind Farm ◽  
Construction Time ◽  
Trade Off

Time, cost and quality are major concerns in construction project management. To achieve a balance between time-cost and time-quality, a trade-off problem among time-cost-quality (TCQ) is proposed for optimisation by the application of a genetic algorithm (GA). A GA attempts to minimise a fitness function that describes the objective to be achieved. The fitness function is specifically designed according to the nature and characteristics of the construction project. By inputting the project parameters, the fitness function should be able to provide a balance between the time, cost and quality of the project. This study applied a GA to strategically search for the best project parameters for an offshore wind farm project to achieve a more accurate prediction for construction time, cost and quality of the project in the pre-construction stage. A series of practical mathematical models are developed through a review of previous studies based on specific merits, and a real offshore wind farm project is studied to identify and verify the applicability and viability of the mathematical models. After the process of optimisation, the results show that the output data is very close to the actual case in terms of construction time, cost and quality.

A New Flatness Evaluation-Rotation Method Based on GA

2010 ◽  
Vol 139-141 ◽  
pp. 2033-2037 ◽  
Author(s):  
Yan Ming Jiang ◽  
Gui Xiong Liu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Three Dimensions ◽  
Local Optimum ◽  
Mutation Probability ◽  
Projection Length ◽  
Rotation Method ◽  
Minimum Zone ◽  
Crossover And Mutation

Flatness is one fundamental element of geometric forms, and the flatness evaluation is particularly important for ensuring the quality of industrial products. This paper presents a new flatness evaluation in the view of the minimum zone evaluation - rotation method based on genetic algorithm. This method determines the minimum zone through rotating measurement points in three dimensions coordinate. The points are firstly rotated about coordinate axes. Then they are projected in one axis, and the smallest projection length is the flatness value. The rotation angles are optimized by genetic algorithm to improve search efficiency. An exponential fitness function and the rotation angles range is designed on the basis of flatness characteristics. An adaptive mode of crossover and mutation probability is used to avoid local optimum. The results show this method can search the minimum zone and converge rapidly.

Using a Neural Network to Determine Fitness in Genetic Design

2001 ◽  
Author(s):  
Shane Farritor ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Design Method ◽  
Fitness Function ◽  
Modular Robots ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Algorithm Search ◽  
Human Designer

Abstract Many automated design approaches require an objective function to determine the quality of a given design. Often, this function depends on a complex relationship between many parameters. Some parameters may be subjective and the relationships difficult to quantify. This paper presents a method where a neural network is used to evaluate the quality of proposed designs during a genetic algorithm search. In general application of the approach, a human designer would propose candidate designs for a given problem. These candidate designs are used to train a neural network fitness function. Then the genetic algorithm evolves new designs that the human designer might not conceive. In this way, the proposed approach would aid in the brainstorming process. The method is applied to the genetic design of modular robots for planetary exploration. This application is briefly described and the genetic design method is summarized. Then the neural network structure is explained and the training method is detailed. Finally, the neural network is used with the genetic design method to create a robot for a specific task.

scholarly journals A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4907
Author(s):  
Ke Zhang ◽  
Zhi Hu ◽  
Yufei Zhan ◽  
Xiaofen Wang ◽  
Keyi Guo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Smart Grid ◽  
Extreme Learning Machine ◽  
Detection Rate ◽  
Fitness Function ◽  
Test Error ◽  
Detection Model ◽  
Learning Machine ◽  
Hidden Layer

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

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