Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

Shi, Jiaxin; Ye, Lin; Li, Zhongwei; Zhan, Dongyang

doi:10.32604/cmes.2022.017467

Cited by 9 publications

(6 citation statements)

References 45 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis has also been used in supporting consensus protocols [152][153][154][155][156][157][158][159][160] For example, Khenfouci et al [125], to avoid data tampering and fraudulent activities, developed a customized clustering-based consensus protocol to carry out a decentralized consensus mechanism, according to which the k-Means was applied locally by multiple competitive miners. The methodology comprised four layers (i.e., data layer, network layer, blockchain layer, and machine learning layer) and had two main actors: management and miner.…”

Section: Category 1: Solitary Implementation Of Unsupervised Learningmentioning

confidence: 99%

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cholevas,

Angeli,

Sereti

et al. 2024

Algorithms

View full text Add to dashboard Cite

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

show abstract

Section: Category 1: Solitary Implementation Of Unsupervised Learningmentioning

confidence: 99%

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Cholevas,

Angeli,

Sereti

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…For the first experiment, the results are shown in Tables 8-10; and the second experimental results are shown in Table 11. In addition, since the UCI datasets are usually more complex than those 2-D synthetic datasets, we conduct the first experiment on only UCI datasets; and in terms of the evaluation metrics, to ensure the reliability of the conclusions, we finally adopt three IVIs (i.e., Sil, CH and DB) and six EVIs (i.e., Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), Homogeneity Score (HOM), Compactness Score (COM), V_measure (V) and Fowlkes and Mallows Index (FMI)) to evaluate the clustering results, which are described in [10,33,34]; except for the DB, all the other indices are found to have the better clustering performance with larger measure values. In addition, Table 10 records the experimental results of the NSDK-means++ and other clustering algorithms; and any evaluation value with better performance than the NSDK-means++ is presented in bold.…”

Section: Performance Evaluation Of the Bwcon-nsdk-means++ Algorithmmentioning

confidence: 99%

An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

Qi¹,

Li²,

Haibin³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

An appropriate optimal number of market segments (ONS) estimation is essential for an enterprise to achieve successful market segmentation, but at present, there is a serious lack of attention to this issue in market segmentation. In our study, an independent adaptive ONS estimation method BWCON-NSDK-means++ is proposed by integrating a new internal validity index (IVI) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions of the existing IVIs, we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two commonly considered measures of compactness and separation. Then, considering the stability, number of parameters and clustering performance, we proposed the NSDK-means++ to participate in the integration where the natural neighbor was used to optimize the initial cluster centers (ICCs) determination strategy in the SDK-means++. At last, to ensure the objectivity of the estimated ONS, we designed a BWCON-based ONS estimation framework that does not require the user to set any parameters in advance and integrated the NSDK-means++ into this framework forming a practical ONS estimation tool BWCON-NSDK-means++. The final experimental results show that the proposed BWCON and NSDK-means++ are significantly more suitable than their respective existing models to participate in the integration for determining the ONS, and the proposed BWCON-NSDK-means++ is demonstrably superior to the BWCON-KMA, BWCON-MBK, BWCON-KM++, BWCON-RKM++, BWCON-SDKM++, BWCON-Single linkage, BWCON-Complete linkage, BWCON-Average linkage and BWCON-Ward linkage in terms of the ONS estimation. Moreover, as an independent market segmentation tool, the BWCON-NSDK-means++ also outperforms the existing models with respect to the inter-market differentiation and sub-market size.

show abstract

“…The coefficient of a signal has been obtained by computing each critical band of the MFCCs signal s individually using l, where s consists of n samples in the current frame. i denotes the current sample of the signal, while f i refers to the total number of sample signals within each frame [28]. The discrete cosine transform contributes to the reduced correlation observed among the MFCCs features.…”

Section: Inertial Sensors Based Features Extractionmentioning

confidence: 99%

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

Batool,

Jalal,

Alazeb

et al. 2023

Preprint

View full text Add to dashboard Cite

Neurological disorders affect bodily functions and there is a growing need for advanced solutions to offer therapeutic advice to patients. A framework using artificial intelligence and machine learning techniques has been proposed to address this. The proposed system employs a combination of RGB, inertial, and depth sensors data. The inertial signals have been filtered using a notch filter to obtain the optimal wearable sensor data by examining the upper and lower cutoff frequencies. Multiple features have been calculated, including mel frequency cepstral coefficients (MFCC), statistical features and Gaussian mixture model (GMM) features. On the other hand, silhouettes have been extracted from RGB and depth images, and four crucial parameters have been employed to gauge the level of accuracy with which patients with neurological disorders performed their activities, including the angle formation between the hands and lower/upper half of the body and center of the body, and the angle between the hands of the silhouette. The resulting features have been fused and classified using principle component analysis and a reweighted genetic algorithm. Evaluation using cross-validation on mRI (multi-modal 3d human pose estimation dataset using mmwave, RGB-D, and inertial sensors) and MHEALTH (mobile health) datasets showed a recognition accuracy rate of 97.29\% and a 97.94\%. The study highlights the need for more datasets to address challenges in rehabilitation using human activity recognition with multi-modal sensors.

show abstract

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

Cited by 9 publications

References 45 publications

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

Diagnosis of Neurological Disorders Rehabilitation Using Effective Feature Elimination and LSTM-RNN Classifier

Contact Info

Product

Resources

About