BYDSEX:二元杨氏双缝实验优化器,用于特征选择的自适应交叉:调查网络入侵检测的性能问题

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Doaa El-Shahat , Mohamed Abdel-Basset , Nourhan Talal , Abduallah Gamal , Mohamed Abouhawwash
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引用次数: 0

摘要

当代技术的进步提供了海量、大维度的数据,导致计算负担沉重。这些海量数据存在不相关、冗余和嘈杂的特征。因此,特征选择(FS)已成为识别最佳特征子集的关键任务。本研究提出了一种带有交叉操作(BYDSEX)的二进制杨氏双光实验优化器(BYDSE),用于解决 FS 问题。此外,所提出的算法采用 V 型传递函数,将标准 YDSE 生成的连续解转换为二进制解。为了评估新的解决方案,我们采用了一种著名的包装方法,即使用欧氏距离度量的 K-Nearest Neighbors (KNN)。我们在所建议的算法中集成了自适应交叉和位和运算,以增强其探索性和群体多样性。此外,比特 AND 运算还能将信息量最大、最有利的特征转移到新的解决方案中。我们使用 31 个大规模数据集将 BYDSEX 与九种最新的强大算法进行了比较,以证明其有效性。此外,我们还利用从 CIC-DDoS2019 和 NSL-KDD 中提取的六个数据集,将 BYDSEX 优化器用于检测大多数物联网设备和当代技术所面临的 DDoS 攻击。利用各种性能指标来评估算法,如准确率、所选特征大小、适配值、适配值和时间。还进行了两种统计检验,如配对样本 T 检验和 Wilcoxon 符号秩检验。在大多数数据集上,BYDSEX 都取得了优于竞争对手的结果。此外,BYDSEX 对 LDAP 和 MSSQL、NETBIOS 和 NSL-KDD 的平均准确率分别为 99.78%、99.89%、99.69% 和 99.48%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BYDSEX: Binary Young's double-slit experiment optimizer with adaptive crossover for feature selection: Investigating performance issues of network intrusion detection
Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the V-shaped transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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