A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges – from Denial of Service (DoS) to privacy attacks – will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into different categories. However, it suffers from the local convergence and sensitivity to selection of the cluster centroids. In this paper, we present a novel fuzzy anomaly detection system that works in two phases. In the first phase – the training phase – we propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase – the detection phase – we employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed algorithm can achieve to the optimal number of clusters, well-separated clusters, as well as increase the high detection rate and decrease the false positive rate at the same time when compared to some other well-known clustering algorithms

Mining and visualizing uncertain data objects and named data networking traffics by fuzzy self-organizing map

Uncertainty is widely spread in real-world data. Uncertain data-in computer science-is typically found in the area of sensor networks where the sensors sense the environment with certain error. Mining and visualizing uncertain data is one of the new challenges that face uncertain databases. This paper presents a new intelligent hybrid algorithm that applies fuzzy set theory into the context of the Self-Organizing Map to mine and visualize uncertain objects. The algorithm is tested in some benchmark problems and the uncertain traffics in Named Data Networking (NDN). Experimental results indicate that the proposed algorithm is precise and effective in terms of the applied performance criteria.Peer ReviewedPostprint (published version

Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit

Abstrak Kredit merupakan suatu pendapatan terbesar bagi bank. Akan tetapi, bank harus selektif dalam menentukan nasabah yang dapat menerima kredit. Permasalahan ini menjadi semakin komplek karena ketika bank salah memberikan kredit kepada nasabah dapat merugikan, selain itu banyaknya parameter penentu dalam penentuan nasabah yang kredit. Clustering merupakan salah satu cara untuk dapat menyelesaikan permasalahan ini. K-means merupakan metode yang simpel dan popular dalam menyelesaikan permasalahan clustering. Akan tetapi, K-means murni tidak dapat memberikan solusi optimum sehingga perlu dilakukan improve untuk mendapatkan solusi optimum. Salah satu metode optimasi yang dapat menyelesaikan permasalahan optimasi dengan baik adalah particle swarm optimization (PSO). PSO sangat membantu dalam proses clustering dengan melakukan optimasi pada titik pusat tiap cluster. Untuk meningkatkan hasil yang lebih baik pada PSO ada beberapa improve yang dilakukan. Pertama penggunaan time-variant inertia untuk membuat nilai w atau inertia dinamis ditiap iterasi. Kedua melakukan kontrol kecepatan partikel atau velocity clamping untuk mendapatkan posisi terbaik. Selain itu untuk mengatasi konvergensi dini dilakukan hybrid PSO dengan random injection. Hasil pengujian menunjukan hybrid PSO K-means memberikan hasil terbesar dibandingkan K-means dan PSO K-means, dimana silhouette dari K-means, PSO K-means, dan hybrid PSO K-means masing-masing 0.57343, 0.792045, 1. Kata kunci: Kredit, Clustering, PSO, K-means, Random Injection Abstract Credit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO). PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1. Keywords: Credit, Clustering, PSO, K-means, Random Injectio

Hybrid K-means Dan Particle Swarm Optimization Untuk Clustering Nasabah Kredit

AbstrakKredit merupakan suatu pendapatan terbesar bagi bank. Akan tetapi, bank harus selektif dalam menentukan nasabah yang dapat menerima kredit. Permasalahan ini menjadi semakin komplek karena ketika bank salah memberikan kredit kepada nasabah dapat merugikan, selain itu banyaknya parameter penentu dalam penentuan nasabah yang kredit. Clustering merupakan salah satu cara untuk dapat menyelesaikan permasalahan ini. K-means merupakan metode yang simpel dan popular dalam menyelesaikan permasalahan clustering. Akan tetapi, K-means murni tidak dapat memberikan solusi optimum sehingga perlu dilakukan improve untuk mendapatkan solusi optimum. Salah satu metode optimasi yang dapat menyelesaikan permasalahan optimasi dengan baik adalah particle swarm optimization (PSO). PSO sangat membantu dalam proses clustering dengan melakukan optimasi pada titik pusat tiap cluster. Untuk meningkatkan hasil yang lebih baik pada PSO ada beberapa improve yang dilakukan. Pertama penggunaan time-variant inertia untuk membuat nilai w atau inertia dinamis ditiap iterasi. Kedua melakukan kontrol kecepatan partikel atau velocity clamping untuk mendapatkan posisi terbaik. Selain itu untuk mengatasi konvergensi dini dilakukan hybrid PSO dengan random injection. Hasil pengujian menunjukan hybrid PSO K-means memberikan hasil terbesar dibandingkan K-means dan PSO K-means, dimana silhouette dari K-means, PSO K-means, dan hybrid PSO K-means masing-masing 0.57343, 0.792045, 1.Kata kunci: Kredit, Clustering, PSO, K-means, Random InjectionAbstractCredit is the biggest revenue for the bank. However, banks have to be selective in deciding which clients can receive the credit. This issue is becoming increasingly complex because when the bank was wrong to give credit to customers can do harm, apart of that a large number of deciding parameter in determining customer credit. Clustering is one way to be able to resolve this issue. K-means is a simple and popular method for solving clustering. However, K-means pure can’t provide optimum solutions so that needs to be done to get the optimum solution to improve. One method of optimization that can solve the problems of optimization with particle swarm optimization is good (PSO). PSO is very helpful in the process of clustering to perform optimization on the central point of each cluster. To improve better results on PSO there are some that do improve. The first use of time-variant inertia to make the dynamic value of inertial w each iteration. Both control the speed of the particle velocity or clamping to get the best position. Besides to overcome premature convergence do hybrid PSO with random injection. The results of this research provide the optimum results for solving clustering of customer credits. The test results showed the hybrid PSO K-means provide the greatest results than K-means and PSO K-means, where the silhouette of the K-means, PSO K-means, and hybrid PSO K-means respectively 0.57343, 0.792045, 1.Keywords: Credit, Clustering, PSO, K-means, Random Injectio

Clustering Nasabah Bank Berdasarkan Tingkat Likuiditas Menggunakan Hybrid Particle Swarm Optimization dengan K-Means

Setiap Bank memiliki layanan dalam meminjamkan modal kepada suatu perusahaan. Namun nominal pinjaman modal tidaklah sedikit. Sehingga untuk mencegah pengembalian modal dapat dilakukan dengan lancar, diperlukan clustering perusahaan berdasarkan analisa likuiditas. Pada penelitian ini, clustering dilakukan menggunakan hybrid Particle Swarm Optimization dengan K-Means (PSO-KMeans). Metode hybrid tersebut digunakan untuk mendapatkan hasil cluster yang tidak terjebak dalam solusi optimum lokal. Hasil yang diperoleh dari hybrid PSO K-Means menunjukkan hasil yang lebih baik jika dibandingkan dengan menggunakan algoritma K-Means tanpa hybrid. Hal ini dibuktikan dengan perolehan centroid terbaik yang ditunjukkan dengan nilai Silhouette Coefficient  yang diperoleh hybrid PSO K-Means lebih baik dibandingkan K-Means

MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks

This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors Lévy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms

Hybrid optimization for k-means clustering learning enhancement

In recent years, combinational optimization issues are introduced as critical problems in clustering algorithms to partition data in a way that optimizes the performance of clustering. K-means algorithm is one of the famous and more popular clustering algorithms which can be simply implemented and it can easily solve the optimization issue with less extra information. But the problems associated with Kmeans algorithm are high error rate, high intra cluster distance and low accuracy. In this regard, researchers have worked to improve the problems computationally, creating efficient solutions that lead to better data analysis through the K-means clustering algorithm. The aim of this study is to improve the accuracy of the Kmeans algorithm using hybrid and meta-heuristic methods. To this end, a metaheuristic approach was proposed for the hybridization of K-means algorithm scheme. It obtained better results by developing a hybrid Genetic Algorithm-K-means (GAK- means) and a hybrid Partial Swarm Optimization-K-means (PSO-K-means) method. Finally, the meta-heuristic of Genetic Algorithm-Partial Swarm Optimization (GAPSO) and Partial Swarm Optimization-Genetic Algorithm (PSOGA) through the K-means algorithm were proposed. The study adopted a methodological approach to achieve the goal in three phases. First, it developed a hybrid GA-based K-means algorithm through a new crossover algorithm based on the range of attributes in order to decrease the number of errors and increase the accuracy rate. Then, a hybrid PSO-based K-means algorithm was mooted by a new calculation function based on the range of domain for decreasing intra-cluster distance and increasing the accuracy rate. Eventually, two meta-heuristic algorithms namely GAPSO-K-means and PSOGA-K-means algorithms were introduced by combining the proposed algorithms to increase the number of correct answers and improve the accuracy rate. The approach was evaluated using six integer standard data sets provided by the University of California Irvine (UCI). Findings confirmed that the hybrid optimization approach enhanced the performance of K-means clustering algorithm. Although both GA-K-means and PSO-K-means improved the result of K-means algorithm, GAPSO-K-means and PSOGA-K-means meta-heuristic algorithms outperformed the hybrid approaches. PSOGA-K-means resulted in 5%- 10% more accuracy for all data sets in comparison with other methods. The approach adopted in this study successfully increased the accuracy rate of the clustering analysis and decreased its error rate and intra-cluster distance

Adaptation of K-means-type algorithms to the Grassmann manifold, An

2019 Spring.Includes bibliographical references.The Grassmann manifold provides a robust framework for analysis of high-dimensional data through the use of subspaces. Treating data as subspaces allows for separability between data classes that is not otherwise achieved in Euclidean space, particularly with the use of the smallest principal angle pseudometric. Clustering algorithms focus on identifying similarities within data and highlighting the underlying structure. To exploit the properties of the Grassmannian for unsupervised data analysis, two variations of the popular K-means algorithm are adapted to perform clustering directly on the manifold. We provide the theoretical foundations needed for computations on the Grassmann manifold and detailed derivations of the key equations. Both algorithms are then thoroughly tested on toy data and two benchmark data sets from machine learning: the MNIST handwritten digit database and the AVIRIS Indian Pines hyperspectral data. Performance of algorithms is tested on manifolds of varying dimension. Unsupervised classification results on the benchmark data are compared to those currently found in the literature

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges – from Denial of Service (DoS) to privacy attacks – will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into different categories. However, it suffers from the local convergence and sensitivity to selection of the cluster centroids. In this paper, we present a novel fuzzy anomaly detection system that works in two phases. In the first phase – the training phase – we propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase – the detection phase – we employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed algorithm can achieve to the optimal number of clusters, well-separated clusters, as well as increase the high detection rate and decrease the false positive rate at the same time when compared to some other well-known clustering algorithms.Peer Reviewe