A many‐objective integrated evolutionary algorithm for feature selection in anomaly detection

Zhang, Zhixia; Xie, Liping

doi:10.1002/cpe.5861

Cited by 10 publications

(8 citation statements)

References 53 publications

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“…Irrelevant and redundant data in network traffic wastes lots of computation and storage resources, and reduces the accuracy of network anomaly detection. Zhang and Xie proposed a multi-objective feature selection algorithm to solve the problem (Zhang and Xie 2020 ). First, the proposed algorithm establishes evolutionary strategy pool and dominance strategy pool, and then designs random probability selection to improve the convergence and diversity of the algorithm.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

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A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2017 ) GA IDS Fast High High Wazirali ( 2021 ) PSO & Binary PSO IDS Fast High Low Zhu et al. ( 2017 ) NSGA-III IDS Fast Medium High Zhang and Xie ( 2020 ) Many-Objective EA Network Anomaly High High High …”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…22 In wrapper techniques, the results of a learning model (classification or regression) are used to evaluate the performance of the generated feature subsets. 23,24 On the other hand, a filter evaluates the generated feature subsets in an unsupervised way independently of ML techniques. In filter methods, the FS process is carried out with respect to unsupervised performance measures, for example, information, similarity, consistency, distance, and statistical test.…”

Section: Fs In Effort Estimationmentioning

confidence: 99%

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Shahpar

Bardsiri

2021

Concurrency and Computation

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Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy-based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global-local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well-known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

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“…In fact, the image information in the data set has certain relevance. 41 The different features of the image have a great impact on the accuracy of the model. Therefore, we introduce LRP to analyze the correlation of image features in the dataset, so as to distinguish the importance of different features (as shown in Figure 2).…”

Section: Layer-wise Relevance Propagationmentioning

confidence: 99%

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

Fan

Cui

2021

Concurrency and Computation

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Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks.ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi-objective optimization model (DPMOM). DPMOM adopts multi-objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA-II algorithm as the basic framework, a multi-objective optimization algorithm based on differential privacy protection (DPP-MOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA-II, IBEA, PESA-II, and AGE-II, DPPMOA is proved that the solution set of this algorithm is better. K E Y W O R D S deep neural networks, differential privacy protection, multi-objective optimization algorithm, multi-objective optimization model INTRODUCTIONPrivate information under the Internet has the characteristics of large amount of data, multiple categories, and complex hierarchical relationships. The existing privacy protection technologies based on anonymization and data encryption need to closely rely on background knowledge assumptions, 1 which can only ensure that privacy on a single data set is not leaked. The limitations are often difficult to meet the requirements for big data privacy protection under the Internet. Therefore, people began to pay attention to privacy protection technologies and their applications such as secure multi-party computing, homomorphic encryption, and differential privacy.K-anonymity 2 protects user privacy by anonymizing data. To prevent quasi-identifiers from forming correspondence with other sets containing key attributes and quasi-identifiers, the key attributes are deleted before the data table is published and the quasi-identifiers are anonymized, making each record indistinguishable from the other records. However, data are protected by deleting the identifier of the traditional data before the data table is released, and the data table is vulnerable to chain attacks, causing user privacy exposure. As an important research direction of cryptography, secure multi-party computation was first proposed by Yao 3 to solve the problem of a group of mutually distrustful participants each holding

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A many‐objective integrated evolutionary algorithm for feature selection in anomaly detection

Cited by 10 publications

References 53 publications

A survey on binary metaheuristic algorithms and their engineering applications

A survey on binary metaheuristic algorithms and their engineering applications

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

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