Improvement of CluStream Algorithm Using Sliding Window for the Clustering of Data Streams

Ahsani, Sahar; Sanati, Morteza Yousef; Mansoorizadeh, Muharram

doi:10.1109/iccke54056.2021.9721505

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The data stream clustering, CluStream, has emerged as a solution to process data arriving at a high rate and synthesize them by a lossless aggregation dividing the clustering into an online component that periodically stores detailed summary statistics and an offline component that uses these summary statistics to provide a quick understanding of map clusters in the data stream [13]. This approach has undergone various improvements and adaptations, such as the use of sliding windows [14,15]; Equi-Clustream, a dynamic clustering of mixed-type time-evolving data [16]; Clustream, which is a Spark implementation [17]; and speed up [18,19].…”

Section: Related Workmentioning

confidence: 99%

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

et al. 2023

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The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

et al. 2023

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“…Yang et al 18 proposed HPStream as a high-dimensional data stream clustering, which via data projection before clustering decreases the dimensionality for stream of data. The CluStream 19 framework has been proposed as an effective way to process the data stream. Based on the proposed method in this study, clustering is divided into online and offline elements.…”

Section: Relevant Workmentioning

confidence: 99%

RAPSAMS: Robust affinity propagation clustering on static android malware stream

Katebi

Rezakhani

Joudaki

et al. 2022

Concurrency and Computation

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This article proposed RAPSAMS, extending affinity propagation (AP) clustering to be robust in malware steaming. We use AP, which has been suggested as an approach for clustering a set of samples that by passing messages in different clusters, represents malware stream clustering. Then, by generating and adding adversarial examples, a method has been proposed to attack this clustering algorithm and try to make a robust algorithm against the proposed attack. Malware clustering has become an active research area in mobile security, in which the knowledge is being exploited in large quantities of the generated malware stream. Different procedures have been used that apply clustering to the malware's static features. This article focuses on selecting the most appropriate examples as centers for Android malware clusters and tries to add some perturbation to fool the clustering algorithm. There are two important challenges in this regard: (i) How to find the best representations for clustering.(ii) How to manage extracted patterns that include important data flow characteristics with different distributions. Malware stream clustering poses many challenges to solve this problem, including dealing with malware that is imported online, being able to process malware quickly and incrementally, dealing appropriately with time constraints, and the way that can manage important features patterns of malware stream.To examine the adaptability of the proposed methods of defense and attack, analyses were performed. The proposed approaches are tested on a couple of malware Android dataset benchmarks, namely, Contagio, Genome, and Drebin. We use permission, API, and intent features of these datasets. The obtained results from false positive rate (FPR) recognize that the amounts of the known algorithm for clustering, AP, increases to more than 50% afterward an offense occurs in a number of instances (like: Genome dataset in permission and intent features). Furthermore, by applying the label flipping attack (LFA) the accuracy measures decrease lower than 85% in all instances. Also, the proposed defense method decreased the FPR value by less than 30%, and the accuracy increased by more than 91%, in all cases. Consequently, the AP clustering will be robust against LFAs.

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Improvement of CluStream Algorithm Using Sliding Window for the Clustering of Data Streams

Cited by 2 publications

References 17 publications

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data

RAPSAMS: Robust affinity propagation clustering on static android malware stream

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