Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

Jiao, Botao; Guo, Yinan; Yang, Shengxiang; Pu, Jiayang; Gong, Dunwei

doi:10.1109/tevc.2022.3232466

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…Machine learning (ML) is becoming increasingly popular in the field of medicine, particularly in the areas of diagnosis and treatment management [ 12 ]. Numerous studies have been conducted to determine how ML can increase the timeliness and precision of diagnosis [ 13 , 14 ]. A crucial element of all healthcare systems around the world is accurate diagnosis.…”

Section: Related Workmentioning

confidence: 99%

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Talpur,

Korejo,

Chandio

et al. 2024

Sensors

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The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) attacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantity and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms (EAs) Optimization with Tree-based Pipelines Optimization Tool (TPOT)-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using EAs, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

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Section: Related Workmentioning

confidence: 99%

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Talpur,

Korejo,

Chandio

et al. 2024

Sensors

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“…AOMSDA is a chunk-based method, which means it lacks the ability to dynamically detect the changes in data streams. To address this limitation, Jiao et al (Jiao et al 2022) propose a reduced-space Multistream Classification based on Multi-objective Optimization (MCMO). It seeks a common feature subset to minimize the distribution shift and then uses a GMM to detect and adapt asynchronous drift.…”

Section: Related Workmentioning

confidence: 99%

“…To demonstrate the superiority of our proposed method, we conducted experiments comparing it with five state-of-the-art methods. Among them, the FUSION (Haque et al 2017) and ATL (Pratama et al 2019) algorithms are based on single-source streams, while the Melanie (Du, Minku, and Zhou 2019), AOMSDA (Renchunzi and Pratama 2022), and MCMO (Jiao et al 2022) are specifically designed for the multi-source classification scenario.…”

Section: Experiments Settingsmentioning

confidence: 99%

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

Yu,

Lu,

Zhang

et al. 2024

AAAI

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Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the temporal dynamic relationships between these streams, leading to the issue of negative transfer arising from irrelevant data. In this paper, we propose a novel Online Boosting Adaptive Learning (OBAL) method that effectively addresses this limitation by adaptively learning the dynamic correlation among different streams. Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy. During the online process, we employ a Gaussian Mixture Model-based weighting mechanism, which is seamlessly integrated with the acquired correlations via AdaCOSA to effectively handle asynchronous drift. This approach significantly improves the predictive performance and stability of the target stream. We conduct comprehensive experiments on several synthetic and real-world data streams, encompassing various drifting scenarios and types. The results clearly demonstrate that OBAL achieves remarkable advancements in addressing multistream classification problems by effectively leveraging positive knowledge derived from multiple sources.

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Smart grid-based manufacturing by nanoparticle analysis with evolutionary optimization probability detection

Wang

2023

Int J Adv Manuf Technol

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Reduced-Space Multistream Classification Based on Multiobjective Evolutionary Optimization

Cited by 6 publications

References 52 publications

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization

Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

Smart grid-based manufacturing by nanoparticle analysis with evolutionary optimization probability detection

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