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Journal ArticleDOI

A Novel Weighted Fuzzy C –Means Clustering Based on Immune Genetic Algorithm for Intrusion Detection

01 Jan 2012-Procedia Engineering (Elsevier)-Vol. 38, pp 1750-1757
TL;DR: A Novel Weighted Fuzzy C-Means clustering method based on Immune Genetic Algorithm (IGA-NWFCM) is proposed and hence it improves the performance of the existing techniques to solve the high dimensional multi-class problems.
About: This article is published in Procedia Engineering.The article was published on 2012-01-01 and is currently open access. It has received 39 citations till now. The article focuses on the topics: Fuzzy clustering & Canopy clustering algorithm.
Citations
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Journal ArticleDOI
01 Nov 2020
TL;DR: A new feature selection algorithm called conditional random field and linear correlation coefficient-based feature selection algorithms to select the most contributed features and classify them using the existing convolutional neural network is proposed.
Abstract: Security is playing a major role in this Internet world due to the rapid growth of Internet users. The various intrusion detection systems were developed by many researchers in the past to identify and detect the intruders using data mining techniques. However, the existing systems are not able to achieve sufficient detection accuracy when using the data mining. For this purpose, we propose a new intrusion detection system to provide security in data communication by identifying and detecting the intruders effectively in wireless networks. Here, we propose a new feature selection algorithm called conditional random field and linear correlation coefficient-based feature selection algorithm to select the most contributed features and classify them using the existing convolutional neural network. The experiments have been conducted for evaluating the proposed intrusion detection system that achieves 98.88% as overall detection accuracy. The tenfold cross-validation has been done for evaluating the performance of the proposed model.

77 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive investigation of the fuzzy misuse detection schemes designed using various machine learning and data mining techniques to deal with different kinds of intrusions.

75 citations

Journal ArticleDOI
Zhaohui Jiang1, Li Tingting1, Min Wenfang1, Zhao Qi1, Rao Yuan1 
TL;DR: F fuzzy C-means clustering based on weights and gene expression programming (WGFCM) is proposed to improve the performance of FCM and is far superior to FCM-based methods in terms of purity, Rand Index, accuracy rate, objective function value and iterative cost.

39 citations


Additional excerpts

  • ...cn classification in data mining, weighted distance for FCM has attracted research interest of many scholars [13,10,18,42,14], and the results are all satisfactory....

    [...]

Journal ArticleDOI
TL;DR: A new intelligent classification model for anomaly detection which detects the intruders effectively in cloud networks using a combination of an enhanced incremental particle swarm optimization and negative selection algorithm is proposed.
Abstract: Internet security is very crucial need in this real world environment due to the rise of e-business, e-learning, and e-governance Intellectual data mining applications are useful for producing security while accessing through the internet from cloud databases Currently, the cloud security researchers are not in a position to introduce more reliable, secure and effective real-time intrusion detection systems for detecting the intruders in online For fulfilling this requirement, we propose a new intelligent classification model for anomaly detection which detects the intruders effectively in cloud networks using a combination of an enhanced incremental particle swarm optimization and negative selection algorithm Moreover, we enhanced these two methods by the uses of Minkowski distance metric for effective decision making The experimental results of the proposed classification model show that this system detects anomalies with low false alarm rate and high detection rate when tested with NSL-KDD dataset which is modified from KDD 1999 Cup dataset

22 citations


Cites background from "A Novel Weighted Fuzzy C –Means Clu..."

  • ...[11] proposed a novel weighted fuzzy C-means clustering based on immune genetic algorithm (IGA-NWFCM) for effective intrusion detection system....

    [...]

Posted Content
01 Oct 2015-viXra
Abstract: Security is a key issue to both computer and computer networks. Intrusion detection System (IDS) is one of the major research problems in network security. IDSs are developed to detect both known and unknown attacks. There are many techniques used in IDS for protecting computers and networks from network based and host based attacks. Various Machine learning techniques are used in IDS. This study analyzes machine learning techniques in IDS. It also reviews many related studies done in the period from 2000 to 2012 and it focuses on machine learning techniques. Related studies include single, hybrid, ensemble classifiers, baseline and datasets used.

19 citations

References
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Book
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

52,705 citations

Book
01 Jan 1996
TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
Abstract: Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.

4,082 citations

Journal ArticleDOI
TL;DR: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming.
Abstract: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming. It's significant to wait for the representative and beneficial books to read. Every book that is provided in better way and utterance will be expected by many peoples. Even you are a good reader or not, feeling to read this book will always appear when you find it. But, when you feel hard to find it as yours, what to do? Borrow to your friends and don't know when to give back it to her or him.

3,932 citations

Journal ArticleDOI
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
Abstract: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data. This function depends on the data set, geometric distance measure, distance between cluster centroids and more importantly on the fuzzy partition generated by any fuzzy algorithm used. The function is mathematically justified via its relationship to a well-defined hard clustering validity function, the separation index for which the condition of uniqueness has already been established. The performance of this validity function compares favorably to that of several others. The application of this validity function to color image segmentation in a computer color vision system for recognition of IC wafer defects which are otherwise impossible to detect using gray-scale image processing is discussed. >

3,237 citations

Journal ArticleDOI
TL;DR: A fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images was shown to be effective and efficient.

326 citations