Data Mining Practical Machine Learning Tools and Techniques

Social Network Analysis

This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

eMedicine创建于1996年，由近万名临床医师作为作者或编辑参与此临床医学知识库的建设，其中编辑均是来自美国哈佛、耶鲁、斯坦福、芝加哥、德克萨斯、加州大学等各分校医学院的教授或副教授。

eMedicine

[서평]「Component Software」 - Beyond Object-Oriented Programming -

http://wstst05.softcomputing.net/pedda.pdf

Modeling intrusion detection system using hybrid intelligent systems

This paper proposes a new perspective for solving systems of complex nonlinear equations by simply viewing them as a multiobjective optimization problem. Every equation in the system represents an objective function whose goal is to minimize the difference between the right and left terms of the corresponding equation. An evolutionary computation technique is applied to solve the problem obtained by transforming the system into a multiobjective optimization problem. The results obtained are compared with a very new technique that is considered as efficient and is also compared with some of the standard techniques that are used for solving nonlinear equations systems. Several well-known and difficult applications (such as interval arithmetic benchmark, kinematic application, neuropsychology application, combustion application, and chemical equilibrium application) are considered for testing the performance of the new approach. Empirical results reveal that the proposed approach is able to deal with high-dimensional equations systems.

/pdf/a-new-approach-for-solving-nonlinear-equations-systems-6ucjrv58na.pdf

A New Approach for Solving Nonlinear Equations Systems

Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

/pdf/hybrid-evolutionary-algorithms-methodologies-architectures-2kg754h5uy.pdf

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic operators, and evolutionary scheme. Several numerical experiments using five benchmarking problems are carried out. Two test problems are taken from PROBEN1 and contain real-world data. The results reveal that Multi-Expression Programming has the best overall behavior for the considered test problems, closely followed by Linear Genetic Programming.

A Comparison of Several Linear Genetic Programming Techniques

A Mobile Ad-Hoc Network (MANET) is a self- configuring network of mobile nodes connected by wire- less links to form an arbitrary topology without the use of existing infrastructure. In this paper, we have studied the effects of various mobility models on the performance of two routing protocols Dynamic Source Routing (DSR- Reactive Protocol) and Destination-Sequenced Distance- Vector (DSDV-Proactive Protocol). For experiment pur- poses, we have considered four mobility scenarios: Ran- dom Waypoint, Group Mobility, Freeway and Manhattan models. These four Mobility Models are selected to rep- resent possibility of practical application in future. Per- formance comparison has also been conducted across varying node densities and number of hops. Experiment results illustrate that performance of the routing protocol varies across different mobility models, node densities and length of data paths.

Crina Grosan

Papers

Modeling intrusion detection system using hybrid intelligent systems

A New Approach for Solving Nonlinear Equations Systems

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

A Comparison of Several Linear Genetic Programming Techniques

Impact of Node Mobility on MANET Routing Protocols Models