An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks

Ruan, Shanshan; Mehmood, Rashid; Daud, Ali; Dawood, Hassan; Alowibdi, Jalal S.

doi:10.1145/3041021.3054148

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…e dataset used to demonstrate the given algorithm is a making-dataset of malware images: visualizations and automatic classi cation documents [19]. is dataset contains 25 clusters of ransomware with several di erent family variants and 56 columns with a 65535 number of rows [6].…”

Section: Methodsmentioning

confidence: 99%

“…e limitations of DP, the difficulty of choosing an appropriate density estimation method, the selection of boundary distances, and the human interpretation required to select the number of cluster centers have been improved in Adaptive-DP [19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…is RE framework can analyze malware les at a multilevel by using binary codes and di erent libraries. e author used a portable executable parser with object code dump Linux-based tools to decode binary-level assembly instructions and DLL, respectively [19].…”

Section: Literature Reviewmentioning

confidence: 99%

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Digital Forensics as Advanced Ransomware Pre-Attack Detection Algorithm for Endpoint Data Protection

Du¹,

Raza

Ahmad

et al. 2022

Security and Communication Networks

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Ransomware is a malicious software that takes files hostage and demands ransomware to release them. It targets individuals, corporations, organizations, and public services such as hospitals and police stations. It is a growing industry that affected more than three million users from 2019 to 2020. The ransom payments totaled 25 billion-plus dollars in the year 2019. The latest version of ransomware was developed using undetectable and nonanalysis techniques. This paper represents an intelligent KNN and density-based machine learning algorithm to detect ransomware pre-attacks on an endpoint system. The data preprocessing and feature engineering techniques are augmented with the KNN algorithm for finding the solution. This helps the anti-malware developer, vendors, endpoint security provider companies, or researchers work on malware detection using advanced machine learning algorithms to develop the more effective ransomware defensive solutions to detect and prevent ransomware pre-attack execution. The proposed KNN and density-based algorithm will predict ransomware detection with higher accuracy than other machine learning algorithms. The anti-malware and anti-ransomware solution provider companies can use this algorithm to improve their existing ransomware detection solutions for endpoint users.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Digital Forensics as Advanced Ransomware Pre-Attack Detection Algorithm for Endpoint Data Protection

Du¹,

Raza

Ahmad

et al. 2022

Security and Communication Networks

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“…Zheng et al proposed an approximate nearest neighbor search method for multiple distance functions with a single index [15]. To overcome the limitations of DPC, an adaptive method was presented in [18] for clustering, where heat-diffusion is used to estimate density and cutoff distance is simplified. In [19], an adaptive density-based clustering algorithm was introduced in spatial databases with noise, which uses a novel adaptive strategy for neighbor selection based on spatial object distribution to improve clustering accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Domain Adaptive Density Clustering Algorithm for Data With Varying Density Distribution

Chen

2021

IEEE Trans. Knowl. Data Eng.

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As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation. To address these problems, we propose a Domain-Adaptive Density Clustering (DADC) algorithm, which consists of three steps: domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble. For data with VDD features, clusters in sparse regions are often neglected by using uniform density peak thresholds, which results in the loss of sparse clusters. We define a domain-adaptive density measurement method based on K-Nearest Neighbors (KNN) to adaptively detect the density peaks of different density regions. We treat each data point and its KNN neighborhood as a subgroup to better reflect its density distribution in a domain view. In addition, for data with ED or MDDM features, a large number of density peaks with similar values can be identified, which results in cluster fragmentation. We propose a cluster center self-identification and cluster self-ensemble method to automatically extract the initial cluster centers and merge the fragmented clusters. Experimental results demonstrate that compared with other comparative algorithms, the proposed DADC algorithm can obtain more reasonable clustering results on data with VDD, ED and MDDM features. Benefitting from a few parameter requirement and non-iterative nature, DADC achieves low computational complexity and is suitable for large-scale data clustering.

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“…However, noise points and multiple densities problems still exist. Ruan et al [25] adopted a heat diffusion method to estimate density and used an adoptive method to select the number of cluster centers. Wang and Song [26] proposed a method to detect the clustering centers automatically by statistical testing.…”

Section: Related Work Of Density Peak Clustering Algorithmmentioning

confidence: 99%

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

Shi

Yan

et al. 2021

IJGI

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Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrary datasets, although it does not perform well when the dataset includes multiple densities. The parameter selection of cut-off distance dc is normally determined by users’ experience and could affect clustering result. In this study, a density-peak-based clustering method is proposed to detect clusters from datasets with multiple densities and shapes. Two improvements are made regarding the limitations of existing clustering methods. First, DPC finds it difficult to detect clusters in a dataset with multiple densities. Each cluster has a unique shape and the interior includes different densities. This method adopts a step by step merging approach to solve the problem. Second, high densities of points can automatically be selected without manual participation, which is more efficient than the existing methods, which require user-specified parameters. According to experimental results, the clustering method can be applied to various datasets and performs better than traditional methods and DPC.

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An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks

Cited by 7 publications

References 32 publications

Digital Forensics as Advanced Ransomware Pre-Attack Detection Algorithm for Endpoint Data Protection

Digital Forensics as Advanced Ransomware Pre-Attack Detection Algorithm for Endpoint Data Protection

A Domain Adaptive Density Clustering Algorithm for Data With Varying Density Distribution

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

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