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THS-IDPC: A three-stage hierarchical sampling method based on improved density peaks clustering algorithm for encrypted malicious traffic detection

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JOURNAL OF SUPERCOMPUTING 2020年第9期76卷 7489-7518页

作者： Chen, Liangchen Gao, Shu Liu, Baoxu Lu, Zhigang Jiang, Zhengwei Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China China Univ Labor Relat Sch Appl Technol Beijing 100048 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China

With the rapid increase in amount of network encrypted traffic and malware samples using encryption to evade identification, detecting encrypted malicious traffic presents challenges. The quality of the encrypted traffic sampling method directly affects the result of malware detection, but most existing machine learning methods for sampling flow-based encrypted traffic data are inherently inaccurate. To solve these problems, an innovative three-stage hierarchical sampling approach based on the improved density peaks clustering algorithm (THS-IDPC) is proposed to enhance the accuracy and efficiency of encrypted malicious traffic detection model. First, we propose an improved density peaks clustering algorithm based on grid screening, custom center decision value and mutual neighbor degree (DPC-GS-MND). In DPC-GS-MND, grid screening effectively reduces the computational complexity and mutual neighbor degree improves the clustering accuracy. Then, we extract and research the three categories features of encrypted traffic data related to malicious activities, and adopt a three-layer hierarchical clustering algorithm based on DPC-GS-MND. Finally, a three-stage sampling approach based on the three-layer hierarchical clustering algorithm (THS-IDPC) is proposed to sample the encrypted traffic data for further deep detection. The experimental results demonstrated that the proposed THS-IDPC is very effective to reduce normal traffic from massive network encrypted traffic simultaneously, and the encrypted malicious traffic detection model with THS-IDPC sampling method can detect multiple encrypted malicious traffic families with higher accuracy and efficiency. Meanwhile, DPC-GS-MND and THS-IDPC have good application prospects in network intrusion detection system under the big data environment.

关键词： Encrypted malicious traffic detection density peaks clustering algorithm Hierarchical clustering data sampling Grid screening Mutual neighbor degree

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An improved density peaks clustering algorithm with fast finding cluster centers

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KNOWLEDGE-BASED SYSTEMS 2018年 158卷 65-74页

作者： Xu, Xiao Ding, Shifei Shi, Zhongzhi China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China

Fast and efficient are common requirements for all clustering algorithms. density peaks clustering algorithm (DPC) can deal with non-spherical clusters well. However, due to the difficulty of large-scale data set storage and its high computational complexity, how to conduct effective data mining has become a challenge. To address this issue, we propose an improved density peaks clustering algorithm with fast finding cluster centers, which improves the efficiency of DPC algorithm by screening points with higher local density based on two novel prescreening strategies. The first strategy is based on the grid-division (GDPC), which screens points according to the density of corresponding grid cells. The second strategy is based on the circle-division (CDPC), which screens the points according to the uneven distribution of data sets in the corresponding circles. Theoretical analysis and experimental results show that both the prescreening strategies can reduce the calculation complexity, and the proposed algorithm not only more satisfied than DPC algorithm, but also superior than well-known Nystrom-SC algorithm on the large-scale data sets. Moreover, due to the different theories of the two prescreening strategies, the first strategy is faster and the second strategy is more accurate on the large-scale data sets.

关键词： density peaks clustering algorithm Prescreening strategy Large-scale data set Decision graph Computational complexity

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A spectral clustering algorithm based on attribute fluctuation and density peaks clustering algorithm

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APPLIED INTELLIGENCE 2023年第9期53卷 10520-10534页

作者： Song, Xin Li, Shuhua Qi, Ziqiang Zhu, Jianlin Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China

Spectral clustering (SC) has become a popular choice for data clustering by converting a dataset to a graph structure and then by identifying optimal subgraphs by graph partitioning to complete the clustering. However, k-means is taken at the clustering stage to randomly select the initial cluster centers, which leads to unstable performance. Notably, k-means needs to specify the number of clusters (prior knowledge). Second, SC calculates the similarity matrix using the linear Euclidean distance, losing part of the effective information. Third, real datasets usually contain redundant features, but traditional SC does not adequately address multi-attribute data. To solve these issues, we propose an SC algorithm based on the attribute fluctuation and density peaks clustering algorithm (AFDSC) to improve the clustering accuracy and effect. Furthermore, to verify the idea of the AFDSC algorithm, we extract the attribute fluctuation factor and propose a histogram clustering algorithm based on attribute fluctuation (AFHC) divorced from spectral clustering. Experimental results show that both the AFDSC algorithm and AFHC algorithm have achieved better performance on fifteen UCI datasets compared with other clustering algorithms.

关键词： Spectral clustering Attribute fluctuation density peaks clustering algorithm Histogram clustering algorithm

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A Fast density peaks clustering algorithm Based on Pre-screening

A Fast Density Peaks Clustering Algorithm Based on Pre-scree...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Xu, Xiao Ding, Shifei Sun, Tongfeng China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781538636497

density peaks clustering algorithm (DPC) is a new density-based clustering algorithm proposed to obtain any shape of the clusters. It finds cluster centers according to the decision graph which drawn based on the density-distance. However, in the process of calculating local density and distance of each point, the time complexity is O(n(2)), which limits the application of DPC. In this paper, we propose a fast density peaks clustering algorithm based on pre-screening (PDPC), which can effectively reduce the calculation complexity on the basis of ensuring the accuracy of clustering. According to the uneven distribution of data sets, the novel pre-screening method is used to remove some points with sparse local density first, and then the cluster centers are selected by using the decision graph. Theoretical analysis and experimental results show that this algorithm can not only reduce the time complexity, but also cluster correctly.

关键词： density peaks clustering algorithm pre-screening large-scale data set decision graph time complexity

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A Secure density peaks clustering algorithm on Cloud Computing 1

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11th International Symposium on Cyberspace Safety and Security (CSS)

作者： Ci, Shang Sun, Liping Liu, Xiaoqing Du, Tingli Zheng, Xiaoyao Anhui Normal Univ Sch Comp & Informat Wuhu Peoples R China Anhui Prov Key Lab Network & Informat Secur Wuhu 241002 Peoples R China

ISBN: (数字)9783030373375

ISBN: (纸本)9783030373375;9783030373368

Cloud computing provides users with the convenience of data out-sourcing computing at risk of privacy leakage, and clustering algorithms have high computational overhead when dealing with large datasets. Aiming at the above problems, this paper presents a security density peak clustering algorithm based on grid in hybrid cloud environment. First, the client uses the homomorphic encryption method to build the encrypted objects with user datasets. Second, the client uploads the encrypted objects to the cloud servers to implement the security protocols proposed in this paper. Finally, the cloud servers return the perturbation clustering results to the client to eliminate the disturbance. In the proposed scheme, only encryption and removing perturbation are performed on the client, ensuring that the client has lower computational complexity. Security analysis and experimental results show that the scheme proposed in this paper can improve the efficiency and accuracy of clustering algorithm under the premise of protecting user privacy.

关键词： Cloud computing security density peaks clustering algorithm Data mining Privacy preserving Homomorphic encryption

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Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

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PERVASIVE AND MOBILE COMPUTING 2024年 105卷

作者： Han, Youjia Wang, Huibin Li, Yueheng Zhang, Lili Hohai Univ Sch Comp & Informat Nanjing 211100 Jiangsu Peoples R China

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSMDPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, badmouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

关键词： density peaks clustering algorithm Security model Trust attacks Wireless sensor networks

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A Hierarchical algorithm Based on density peaks clustering and Ant Colony Optimization for Traveling Salesman Problem

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IEEE ACCESS 2018年 6卷 38921-38933页

作者： Liao, Erchong Liu, Changan North China Elect Power Univ Sch Control & Comp Engn Baoding 071003 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

This paper proposed a hierarchical hybrid algorithm for traveling salesman problem (TSP) according to the idea of divide-and-conquer. The TSP problem is decomposed into a few subproblems with small-scale nodes by density peaks clustering algorithm. Every subproblem is resolved by ant colony optimization algorithm, this is the lower layer. The center nodes of all subproblems constitute a new TSP problem, which forms the upper layer. All local tours of these subproblems are joined to generate the initial global tour in the same order that the center nodes are traversed in the upper layer TSP problem. Finally, the global tour is optimized by k-Opt algorithms. Thirty benchmark instances taken from TSPLIB are divided into three groups on the basis of problem size: small-scale, large-scale, and very large-scale. The experimental result shows that the proposed algorithm can obtain the solutions with higher accuracy and stronger robustness, and significantly reduce runtime, especially for the very large-scale TSP problem.

关键词： Ant colony optimization density peaks clustering algorithm k-Opt algorithm traveling salesman problem

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density peaks algorithm based on information entropy and merging strategy for power load curve clustering

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 8801-8832页

作者： Yang, Yumeng Wang, Li Cheng, Zizhen Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

To solve the problems of density peaks clustering (DPC) algorithm sensitive to cutoff distance and subjectivity of clustering center selection, we propose an improved density peaks algorithm based on information entropy and merging strategy (DPC-IEMS) for realizing power load curve clustering. First, a cutoff distance optimization method based on information entropy is proposed. This method uses sparrow search algorithm (SSA) to find the minimum value of information entropy about the product of local density and relative distance to calculate the optimal cutoff distance suitable for the load datasets. Then, a merging strategy is proposed to realize the adaptive selection of clustering centers. This strategy first generates a large number of initial sub-clusters by DPC, and then merges the sub-clusters using the fusion condition until the final iteration condition is satisfied. The performance of DPC-IEMS algorithm is evaluated on the U.S. load datasets and the Chinese load datasets, and the effectiveness and practicality of DPC-IEMS algorithm for power load curve clustering are fully demonstrated.

关键词： Power load curve clustering density peaks clustering algorithm Information entropy Merging strategy

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Differential Privacy-Preserving density peaks clustering Based on Shared Near Neighbors Similarity

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IEEE ACCESS 2019年 7卷 89427-89440页

作者： Sun, Liping Bao, Shuting Ci, Shang Zheng, Xiaoyao Guo, Liangmin Luo, Yonglong Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Peoples R China Anhui Normal Univ Anhui Prov Key Lab Network & Informat Secur Wuhu 241002 Peoples R China

density peaks clustering is a novel and efficient density-based clustering algorithm. However, the problem of the sensitive information leakage and the associated security risk with the applications of clustering methods is rarely considered. To address the problem, we proposed differential privacy-preserving density peaks' clustering based on the shared near neighbors similarity method in this paper. First, the Euclidean distance and the shared near neighbors similarity were combined to define the local density of a sample, and the Laplace noise was added to the local density and the shortest distance to protect privacy. Second, the process of cluster center selection was optimized to select the initial cluster centers based on the neighborhood information. Finally, each sample was assigned to the cluster as its nearest neighbor with higher local density. The experimental results on both the UCI and synthetic datasets show that compared with other algorithms, our method more effectively protects the data privacy and improves the quality of the clustering results.

关键词： Privacy preservation differential privacy density peaks clustering algorithm shared near neighbors similarity

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The Improvement on Self-Adaption Select Cluster Centers Based on Fast Search and Find of density peaks clustering 16

The Improvement on Self-Adaption Select Cluster Centers Base...

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16th International Conference on Computational Intelligence and Security (CIS)

作者： Du, Hui Ni, Yiyang Northwest Normal Univ Lanzhou Peoples R China

ISBN: (纸本)9781665404457

In order to solve the problem of manual selection of cluster centers in density peaks clustering algorithm, an automatic selection algorithm of cluster centers was proposed in this paper, which can calculate the change rate and difference for each data. Firstly, the local density rho and the high density nearest distance delta of each data point were multiplied and sorted to calculate the difference value A between two adjacent data points, where A is a group of finite sequences from big to small, and the ratio of each item in the sequence to its next term is theta. Through the threshold range of theta and Delta, the cluster centers can be selected adaptively, and the number of clusters can be determined automatically. Experiment results have shown that the algorithm is suitable for non-convex data with good clustering effect.

关键词： component density peaks clustering algorithm change rate difference self-adaption

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