Journal of Artificial Intelligence and Soft Computing Research最新文献_第9页

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection 一种提高入侵检测性能的自动编码器增强堆叠神经网络模型

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-04-01 DOI: 10.2478/jaiscr-2022-0010

Csaba Brunner, Andrea Ko, Szabina Fodor

{"title":"An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection","authors":"Csaba Brunner, Andrea Ko, Szabina Fodor","doi":"10.2478/jaiscr-2022-0010","DOIUrl":"https://doi.org/10.2478/jaiscr-2022-0010","url":null,"abstract":"Abstract Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoen-coder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"12 1","pages":"149 - 163"},"PeriodicalIF":2.8,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46009423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems 基于多Agent的协同入侵检测系统综述

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-29 DOI: 10.2478/jaiscr-2021-0008

Nassima Bougueroua, S. Mazouzi, Mohamed Belaoued, N. Seddari, A. Derhab, A. Bouras

{"title":"A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems","authors":"Nassima Bougueroua, S. Mazouzi, Mohamed Belaoued, N. Seddari, A. Derhab, A. Bouras","doi":"10.2478/jaiscr-2021-0008","DOIUrl":"https://doi.org/10.2478/jaiscr-2021-0008","url":null,"abstract":"Abstract Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"11 1","pages":"111 - 142"},"PeriodicalIF":2.8,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43194730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention 应用中的2型模糊逻辑系统：用于空气污染预防的选择性催化还原中的数据管理

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-29 DOI: 10.2478/jaiscr-2021-0006

A. Niewiadomski, Marcin Kacprowicz

引用次数: 4

Hardware Rough Set Processor Parallel Architecture in FPGA for Finding Core in Big Datasets 基于FPGA的硬件粗糙集处理器并行结构在大数据集中寻核

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-29 DOI: 10.2478/jaiscr-2021-0007

M. Kopczynski, T. Grzes

引用次数: 5

An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory 证据理论框架下直觉模糊Topsis方法的一种推广方法

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-29 DOI: 10.2478/jaiscr-2021-0010

Ludmila Dymova, Krzysztof Kaczmarek, Pavel V. Sevastjanov, L. Sulkowski, K. Przybyszewski

{"title":"An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory","authors":"Ludmila Dymova, Krzysztof Kaczmarek, Pavel V. Sevastjanov, L. Sulkowski, K. Przybyszewski","doi":"10.2478/jaiscr-2021-0010","DOIUrl":"https://doi.org/10.2478/jaiscr-2021-0010","url":null,"abstract":"Abstract A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problems. This boosts considerably the quality of aggregating operators used in the intuitionistic fuzzy TOPSIS method. It is pointed out that the conventional TOPSIS method may be naturally treated as a weighted sum of some modified local criteria. Because this aggregating approach does not always reflects well intentions of decision makers, two additional aggregating methods that cannot be defined in the framework of conventional A IFS based on local criteria weights being intuitionistic fuzzy values, are introduced. Having in mind that different aggregating methods generally produce different alternative rankings to obtain the compromise ranking, the method for aggregating of aggregation modes has been applied. Some examples are used to illustrate the validity and features of the proposed approach.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"11 1","pages":"157 - 175"},"PeriodicalIF":2.8,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47401026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network 利用递归神经网络监测蒸汽电厂蓄热式换热器

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-29 DOI: 10.2478/jaiscr-2021-0009

Tacjana Niksa-Rynkiewicz, Natalia Szewczuk-Krypa, A. Witkowska, K. Cpałka, Marcin Zalasiński, A. Cader

引用次数: 10

Artificial Intelligence and Soft Computing: 20th International Conference, ICAISC 2021, Virtual Event, June 21–23, 2021, Proceedings, Part II 人工智能与软计算:第20届国际会议，ICAISC 2021，虚拟事件，2021年6月21日至23日，会议录，第二部分

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87897-9

引用次数: 0

Artificial Intelligence and Soft Computing: 20th International Conference, ICAISC 2021, Virtual Event, June 21–23, 2021, Proceedings, Part I 人工智能与软计算:第20届国际会议，ICAISC 2021，虚拟事件，2021年6月21日至23日，会议录，第一部分

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87986-0

引用次数: 0

On Knowledge Discovery and Representations of Molecular Structures Using Topological Indices 基于拓扑指数的分子结构知识发现与表示

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2020-12-03 DOI: 10.2478/jaiscr-2021-0002

F. Alsaadi, Syed Ahtsham ul Haq Bokhary, Aqsa Shah, Usman Ali, Jinde Cao, M. Alassafi, M. U. Rehman, J. Rahman

引用次数: 4

A Novel Method for Invariant Image Reconstruction 一种新的图像不变性重建方法

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2020-12-03 DOI: 10.2478/jaiscr-2021-0005

M. Pawlak, G. Panesar, M. Korytkowski

引用次数: 4