Intrusion detection system(IDS)techniques are used in cybersecurity to protect and safeguard sensitive assets.The increasing network security risks can be mitigated by implementing effective IDS methods as a defense m...Intrusion detection system(IDS)techniques are used in cybersecurity to protect and safeguard sensitive assets.The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism.The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor(FKNN)algorithm.Using this method,two parameters,i.e.,the neighborhood size(k)and fuzzy strength parameter(m)were characterized by implementing the particle swarm optimization(PSO).In addition to being used for FKNN parametric optimization,PSO is also used for selecting the conditional feature subsets for detection.To proficiently regulate the indigenous and comprehensive search skill of the PSO approach,two control parameters containing the time-varying inertia weight(TVIW)and time-varying acceleration coefficients(TVAC)were applied to the system.In addition,continuous and binary PSO algorithms were both executed on a multi-core platform.The proposed IDS model was compared with other state-of-the-art classifiers.The results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy,precision,recall,and f-score.The results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two datasets.Moreover,the proposed method was able to obtain a large number of true positives and negatives,with minimal number of false positives and negatives.展开更多
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-n...Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.展开更多
Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl...Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.展开更多
文摘Intrusion detection system(IDS)techniques are used in cybersecurity to protect and safeguard sensitive assets.The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism.The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor(FKNN)algorithm.Using this method,two parameters,i.e.,the neighborhood size(k)and fuzzy strength parameter(m)were characterized by implementing the particle swarm optimization(PSO).In addition to being used for FKNN parametric optimization,PSO is also used for selecting the conditional feature subsets for detection.To proficiently regulate the indigenous and comprehensive search skill of the PSO approach,two control parameters containing the time-varying inertia weight(TVIW)and time-varying acceleration coefficients(TVAC)were applied to the system.In addition,continuous and binary PSO algorithms were both executed on a multi-core platform.The proposed IDS model was compared with other state-of-the-art classifiers.The results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy,precision,recall,and f-score.The results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two datasets.Moreover,the proposed method was able to obtain a large number of true positives and negatives,with minimal number of false positives and negatives.
基金Supported by the National Key Research and Development Program (No.2019YFA0707201)the Key Work Program of Institute of Scientific and Technical Information of China (No.ZD2022-01,ZD2023-07)。
文摘Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.
文摘Matrix principal component analysis （MatPCA）, as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA （F-WMatPCA）is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method（FKNN） to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces.
