The Experts below are selected from a list of 218022 Experts worldwide ranked by ideXlab platform
Ali Selamat - One of the best experts on this subject based on the ideXlab platform.
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A Swarm Negative Selection Algorithm for Email Spam Detection
2016Co-Authors: Ismaila Idris, Ali SelamatAbstract:A Swarm Negative Selection Algorithm for Email Spam Detection The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative Selection Algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The local outlier factor is implemented as fitness function to determine the local best (Pbest) of each candidate detector. Velocity and position of particle swarm optimization is employed to support the movement and new particle position of each outlier detector. The particle swarm optimization (PSO) is implemented to improve detector generation in negative Selection Algorithm rather than the random generation of detectors. The model is called swarm negative Selection Algorithm (SNSA). The experimental result show that the proposed SNSA model performs better than the standard NSA.
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A Swarm Negative Selection Algorithm for Email Spam
2015Co-Authors: Ali Selamat, Ismaila IdrisAbstract:The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative Selection Algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The local outlier factor is implemented as fitness function to determine the local best (Pbest) of each candidate detector. Velocity and position of particle swarm optimization is employed to support the movement and new particle position of each outlier detector. The particle swarm optimization (PSO) is implemented to improve detector generation in negative Selection Algorithm rather than the random generation of detectors. The model is called swarm negative Selection Algorithm (SNSA). The experimental result show that the proposed SNSA model performs better than the standard NSA.
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Improved email spam detection model with negative Selection Algorithm and particle swarm optimization
Applied Soft Computing, 2014Co-Authors: Ismaila Idris, Ali SelamatAbstract:The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous Algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative Selection Algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative Selection Algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA-PSO is higher than the standard negative Selection Algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. Negative Selection Algorithm is 68.86% and the proposed hybrid negative Selection Algorithm with particle swarm optimization is 91.22%.
Ismaila Idris - One of the best experts on this subject based on the ideXlab platform.
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A Swarm Negative Selection Algorithm for Email Spam Detection
2016Co-Authors: Ismaila Idris, Ali SelamatAbstract:A Swarm Negative Selection Algorithm for Email Spam Detection The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative Selection Algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The local outlier factor is implemented as fitness function to determine the local best (Pbest) of each candidate detector. Velocity and position of particle swarm optimization is employed to support the movement and new particle position of each outlier detector. The particle swarm optimization (PSO) is implemented to improve detector generation in negative Selection Algorithm rather than the random generation of detectors. The model is called swarm negative Selection Algorithm (SNSA). The experimental result show that the proposed SNSA model performs better than the standard NSA.
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A Swarm Negative Selection Algorithm for Email Spam
2015Co-Authors: Ali Selamat, Ismaila IdrisAbstract:The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative Selection Algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The local outlier factor is implemented as fitness function to determine the local best (Pbest) of each candidate detector. Velocity and position of particle swarm optimization is employed to support the movement and new particle position of each outlier detector. The particle swarm optimization (PSO) is implemented to improve detector generation in negative Selection Algorithm rather than the random generation of detectors. The model is called swarm negative Selection Algorithm (SNSA). The experimental result show that the proposed SNSA model performs better than the standard NSA.
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Improved email spam detection model with negative Selection Algorithm and particle swarm optimization
Applied Soft Computing, 2014Co-Authors: Ismaila Idris, Ali SelamatAbstract:The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous Algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative Selection Algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative Selection Algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA-PSO is higher than the standard negative Selection Algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. Negative Selection Algorithm is 68.86% and the proposed hybrid negative Selection Algorithm with particle swarm optimization is 91.22%.
Dipankar Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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negative Selection Algorithm for aircraft fault detection
International Conference on Artificial Immune Systems, 2004Co-Authors: Dipankar Dasgupta, K Krishnakumar, D Wong, M BerryAbstract:We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection Algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.
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real valued negative Selection Algorithm with variable sized detectors
Genetic and Evolutionary Computation Conference, 2004Co-Authors: Dipankar DasguptaAbstract:A new scheme of detector generation and matching mechanism for negative Selection Algorithm is introduced featuring detectors with variable properties. While detectors can be variable in different ways using this concept, the paper describes an Algorithm when the variable parameter is the size of the detectors in real-valued space. The Algorithm is tested using synthetic and real-world datasets, including time series data that are transformed into multiple-dimensional data during the preprocessing phase. Preliminary results demonstrate that the new approach enhances the negative Selection Algorithm in efficiency and reliability without significant increase in complexity.
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a randomized real valued negative Selection Algorithm
International Conference on Artificial Immune Systems, 2003Co-Authors: Fabio A Gonzalez, Dipankar Dasgupta, Luis Fernando NinoAbstract:This paper presents a real-valued negative Selection Algorithm with good mathematical foundation that solves some of the drawbacks of our previous approach [11]. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization Algorithm with proven convergence properties. The proposed method is a randomized Algorithm based on Monte Carlo methods. Experiments are performed to validate the assumptions made while designing the Algorithm and to evaluate its performance.
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Anomaly detection in multidimensional data using negative Selection Algorithm
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 1Co-Authors: Dipankar Dasgupta, Nivedita Sumi MajumdarAbstract:While dealing with sensitive personnel data, the data have to be maintained to preserve integrity and usefulness. The mechanisms of the natural immune system are very promising in this area, it being an efficient anomaly or change detection system. This paper reports anomaly detection results with single and multidimensional data sets using the negative Selection Algorithm developed by Forrest et al. (1994).
Lu Chang-hui - One of the best experts on this subject based on the ideXlab platform.
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New materialized view Selection Algorithm
Journal of Computer Applications, 2009Co-Authors: Lu Chang-huiAbstract:To address the problem that the current materialized view Selection methods couldn't solve random OLAP query efficiently,this paper proposed a 2-Phases Materialized View Selection(2-PMVS) Algorithm which combined static materialized view Selection Algorithm with dynamic materialized view Selection Algorithm.It could dynamically bridge the gap between random queries and default queries.The experiments validate the effectiveness of the Algorithm.
Jun Zhang - One of the best experts on this subject based on the ideXlab platform.
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A Fast Satellite Selection Algorithm: Beyond Four Satellites
IEEE Journal of Selected Topics in Signal Processing, 2009Co-Authors: Miaoyan Zhang, Jun ZhangAbstract:Satellite Selection is an important executive logic to prevent unnecessary navigation signals in the first place for Global Positioning System (GPS) receivers with limited number of channels and real-time processing power, such as those used in mobile phones, cars, and space crafts. In this paper, we propose a fast satellite Selection Algorithm to select more than four satellites based on the optimal geometries, which can obtain the smallest geometric dilution of precision (GDOP) values. The main idea of this fast Algorithm is to select a subset of all satellites in view whose geometry is the most similar to the optimal geometry. Computer simulation shows that the consumed time of this Algorithm is very close to that of the quasi-optimal satellite Selection Algorithm and obviously lower than that of the traditional optimal satellite Selection Algorithm to minimize GDOP factor, but the increased GDOP values relative to the minimal GDOP values are much smaller than those of the quasi-optimal satellite Selection Algorithm.
