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An on-the-fly framework for usable access control policy mining

COMPUTERS & SECURITY 2025年 150卷

作者： Guo, Yunchuan Wang, Xiao Yu, Mingjie Li, Fenghua Pang, Zhen Fang, Liang Chinese Acad Sci Inst Informat Engn Beijing 100095 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Key Lab Cyberspace Secur Def Beijing 100085 Peoples R China Univ Sci & Technol China Sch Cyber Secur Hefei 230027 Peoples R China Xiyuan Hosp CACMS Beijing 100091 Peoples R China

Access control policy mining, which extracts or infers access control policies from existing system logs or configurations within a given environment, is widely applied in policy migration. No policy mining algorithm can produce completely accurate policies, necessitating human intervention to correct errors before implementation. However, humans may fail to revise the produced policies without fully understanding all permission details. In this paper, we propose an on-the-fly framework to assist human revisions by mining usable policies. This framework is formulated as an approximate optimization problem, designed to avoid permission errors and redundancy by balancing submodularity and modular costs through an iterative search for access rules. The search space is guided by two pruning techniques: a tight optimistic estimate to only eliminate unpromising candidates and a queue cutter to sample promising candidates in advance. Experimental evaluations on two real-world and three publicly available synthetic datasets indicate that: 1) our method produces more concise results than existing methods, achieving a 93.2% reduction in redundancy;2) our method is at least five times faster than state-of-the-art approaches. To further validate the usability of the policies obtained by our approach, we conducted a user study involving 30 participants and 7 large language models (LLMs). The results show that 90.9% of participants, including LLMs such as gpt-4o-mini, successfully modified our mined policies to meet given permission goals.

关键词： Access control Policy mining Policy migration Usability Submodular optimization approximate algorithm

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Heuristic and approximate Steiner tree algorithms for ensuring network connectivity in mobile wireless sensor networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2025年 238卷

作者： Son, Nguyen Van Hanh, Nguyen Thi Minh, Trinh The Binh, Huynh Thi Thanh Thang, Nguyen Xuan PHENIKAA Univ Fac Comp Sci Hanoi 12116 Vietnam Phenikaa Univ Fac Interdisciplinary Digital Technol Hanoi 12116 Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Hanoi Univ Hanoi Vietnam

Connectivity problems are among the most challenging issues in Mobile Wireless Sensor Networks (MWSNs). Ensuring connectivity in such networks means finding network configurations in which all mobile sensors can connect to a base station during data gathering events. This paper considers MWSNs in which a minimal number of relay nodes need to be placed in order to maintain connectivity. Two algorithms are proposed: AST (Approximation Steiner Tree) is an approximate algorithm with a ratio of 2 x opt + O(K x N) (where K x N is the number of nodes on the time-flattened domain) and CBAST (Cluster-Based on the Approximation Steiner Tree algorithm) is a highly effective heuristic. Both algorithms focus on optimal Steiner Tree construction to produce high-quality solutions. AST is an approximation based on 3-point Steiner Trees, while CBAST forms clusters of static components and uses a 2-approximation algorithm to maintain connectivity in each cluster. Experiments on a large number of generated instances are performed to compare AST and CBAST with existing state-of-the-art heuristics. Our results show that CBAST significantly outperforms baseline methods while also reducing computation time and energy consumption.

关键词： Connectivity Mobile wireless sensor networks approximate algorithm Steiner tree Spanning tree

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A parallel approximate SS-ELM algorithm based on MapReduce for large-scale datasets

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2017年 108卷 85-94页

作者： Chen, Cen Li, Kenli Ouyang, Aijia Li, Keqin Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA Zunyi Normal Coll Dept Informat Engn Zunyi 563006 Guizhou Peoples R China

Extreme Learning Machine (ELM) algorithm not only has gained much attention of many scholars and researchers, but also has been widely applied in recent years especially when dealing with big data because of its better generalization performance and learning speed. The proposal of SS-ELM (semi-supervised Extreme Learning Machine) extends ELM algorithm to the area of semi-supervised learning which is an important issue of machine learning on big data. However, the original SS-ELM algorithm needs to store the data in the memory before processing it, so that it could not handle large and web-scale data sets which are of frequent appearance in the era of big data. To solve this problem, this paper firstly proposes an efficient parallel SS-ELM (PSS-ELM) algorithm on MapReduce model, adopting a series of optimizations to improve its performance. Then, a parallel approximate SS-ELM algorithm based on MapReduce (PASS-ELM) is proposed. PASS-ELM is based on the approximate adjacent similarity matrix (AASM) algorithm, which leverages the Locality-Sensitive Hashing (LSH) scheme to calculate the approximate adjacent similarity matrix, thus greatly reducing the complexity and occupied memory. The proposed AASM algorithm is general, because the calculation of the adjacent similarity matrix is the key operation in many other machine learning algorithms. The experimental results have demonstrated that the proposed PASS-ELM algorithm can efficiently process very large-scale data sets with a good performance, without significantly impacting the accuracy of the results. (C) 2017 Elsevier Inc. All rights reserved.

关键词： PASS-ELM MapReduce LSH Parallel approximate algorithm Big data

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Exact and approximate algorithms for discounted {0-1} knapsack problem

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INFORMATION SCIENCES 2016年第0期369卷 634-647页

作者： He, Yi-Chao Wang, Xi-Zhao He, Yu-Lin Zhao, Shu-Liang Li, Wen-Bin Hebei GEO Univ Coll Informat Engn Shijiazhuang 050031 Hebei Peoples R China Shenzhen Univ Big Data Inst Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Hebei Normal Univ Coll Math & Informat Sci Shijiazhuang 050024 Hebei Peoples R China

The Discounted {0-1} Knapsack Problem (D{0-1}KP) is an extension of the classical 0-1 knapsack problem (0-1 KP) that consists of selecting a set of item groups where each group includes three items and at most one of the three items can be selected. The D{0-1}KP is more challenging than the 0-1 KP because four choices of items in an item group diversify the selection of the items. In this paper, we systematically studied the exact and approximate algorithms for solving D{0-1}KP. Firstly, a new exact algorithm based on the dynamic programming and its corresponding fully polynomial time approximation scheme were designed. Secondly, a 2-approximation algorithm for D{0-1}KP was developed. Thirdly, a greedy repair algorithm for handling the infeasible solutions of D{0-1}KP was proposed and we further studied how to use binary particle swarm optimization and greedy repair algorithm to solve the D{0-1}KP Finally, we used four different kinds of instances to compare the approximate rate and solving time of the exact and approximate algorithms. The experimental results and theoretical analysis showed that the approximate algorithms worked well for D{0-1}KP instances with large value, weight, and size coefficients, while the exact algorithm was good at solving D{0-1}KP instance's with small value, weight, and size coefficients. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Discounted {0-1} knapsack problem Exact algorithm approximate algorithm Dynamic programming Particle swarm optimization

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approximate Minimum Enclosing Ball algorithm with Smaller Core Sets for Binary Support Vector Machine

Approximate Minimum Enclosing Ball Algorithm with Smaller Co...

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22nd Chinese Control and Decision Conference

作者： Wang, Yongqing Li, Yan Chang, Liang ZhengZhou Inst Aeronaut Ind Management Dept Comp Sci & Applicat Zhengzhou 450015 Peoples R China ZhengZhou Inst Aeronaut Ind Management Comp Ctr Zhengzhou 450015 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Beijing Normal Univ Virtual Reality & Visualizat Technol Inst Beijing 100875 Peoples R China

ISBN: (纸本)9781424451821

Core Vector Machine (CVM) is a promising technique for scaling up a binary Support Vector Machine (SVM) to handle large data sets with the utilization of approximate Minimum Enclosing Ball (MEB) algorithm. However, the experimental results in implementation show that there always exists some redundancy in the final core set to determine the final decision function. We propose an approximate MEB algorithm in this paper to decrease the redundant core vectors as much as possible. The simulations on synthetic data sets demonstrate the competitive performances on training time, core vectors' number and training accuracy.

关键词： Support Vector Machine Minimum Enclosing Ball approximate algorithm Core vector

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Exact and approximate algorithms for Computing Betweenness Centrality in Directed Graphs

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FUNDAMENTA INFORMATICAE 2021年第3期182卷 219-242页

作者： Chehreghani, Mostafa Haghir Bifet, Albert Abdessalem, Talel Amirkabir Univ Technol Dept Comp Engn Tehran Polytech Tehran Iran Telecom Paris IP Paris LTCI Paris France

Graphs (networks) are an important tool to model data in different domains. Real-world graphs are usually directed, where the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network G and a vertex r is an element of V(G), we propose an exact algorithm to compute betweenness score of r. Our algorithm pre-computes a set RV(r), which is used to prune a huge amount of computations that do not contribute to the betweenness score of r. Time complexity of our algorithm depends on vertical bar RV(r)vertical bar and it is respectively Theta(vertical bar RV(r)vertical bar . vertical bar E(G)vertical bar) and Theta(vertical bar RV(r)vertical bar . vertical bar E(G)vertical bar + vertical bar RV(r)vertical bar . vertical bar V (G)vertical bar log vertical bar V (G)vertical bar) for unweighted graphs and weighted graphs with positive weights. jRV(r)j is bounded from above by vertical bar V (G)vertical bar - 1 and in most cases, it is a small constant. Then, for the cases where RV(r) is large, we present a simple randomized algorithm that samples from RV(r) and performs computations for only the sampled elements. We show that this algorithm provides an (epsilon, delta)-approximation to the betweenness score of r. Finally, we perform extensive experiments over several real-world datasets from different domains for several randomly chosen vertices as well as for the vertices with the highest betweenness scores. Our experiments reveal that for estimating betweenness score of a single vertex, our algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy. Our experiments also reveal that our algorithm improves the existing algorithms when someone is interested in computing betweenness values of the vertices in a set whose cardinality is very small.

关键词： Social networks directed graphs betweenness centrality exact algorithm approximate algorithm

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Fast algorithm for approximate k-Nearest Neighbor Graph Construction3

Fast Algorithm for Approximate <i>k</i>-Nearest Neighbor Gra...

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IEEE 13th International Conference on Data Mining (ICDM)

作者： Wang, Dilin Shi, Lei Cao, Jianwen Chinese Acad Sci ISCAS Lab Parallel Software & Computat Sci Software Beijing 100049 Peoples R China Chinese Acad Sci ISCAS State Key Lab Comp Sci Beijing 100049 Peoples R China Grad Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9780769551098

The k-Nearest Neighbor (k-NN) graphs are widely used in data mining and machine learning. How to construct a high quality k-NN graph for generic similarity measures efficiently is crucial for many applications. In this paper, we propose a new approach to effectively and efficiently construct an approximate k-NN graph. Our framework is as follows: (1) generate a random k-NN graph approximation, G(f);(2) perform random hierarchical partitions of the space to construct an approximate neighborhood graph G(p), which is then combined with graph G(f) to yield a more accurate graph G(m);(3) neighborhood propagation is conducted on G(m) to further enhance the accuracy, and output the solution as graph G(f);(4) repeat the process of (2) and (3) several times until a reasonable solution is reached. The experiments on a variety of real data sets and a high intrinsic dimensional synthetic data set verify the high performance of the proposed method and demonstrate that it is superior to previous state-of-the-art k-NN graph construction approaches.

关键词： k-nearest neighbor graph multiple random division approximate algorithm

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A cost-provable solution for reliable in-network computing-enabled services deployment

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COMPUTER NETWORKS 2025年 257卷

作者： Liu, Xiaorong Zheng, Danyang Xing, Huanlai Feng, Li Peng, Chengzong Cao, Xiaojun Southwest Jiaotong Univ Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Sichuan Peoples R China Chengdu Univ Informat & Technol Chengdu Sichuan Peoples R China Georgia State Univ Comp Sci Dept Atlanta GA USA

Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable INCenabled services, redundant task replicas are hosted by network devices to meet a specified service reliability threshold such as 99.9%, 99.99%, and 99.999%. Most existing works assume that this threshold is solely impacted by the software reliability while neglecting the hardware reliability. This neglectedness likely leads to unexpected service interruptions when the software replicas are co-deployed over one unreliable hardware. This work jointly considers the heterogeneous reliability brought by both software and hardware and identifies a novel phenomenon called "Software-Reliability-Only Experience Degradation"(SRO-ED). To address this, we mathematically establish the INC-enabled services adoption with heterogeneous reliability (ISAHR) problem to optimize service costs and prove its NP-hardness. We introduce an effective Cost-Reliability (CR) measure to indicate the average cost needed to satisfy each reliability unit while considering both software and hardware reliabilities. Next, we propose an innovative algorithm called CR measure-based INC services deployment (CRD), which is proved to be logarithm-approximate in cost optimization. Extensive simulation results validate the logarithmic approximation of CR-D, and show that it outperforms the benchmarks by an average of 29.42% and 35.77% in cost optimization.

关键词： In-network computing (INC) Cost optimization approximate algorithm

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Developing approximate algorithms for EPQ problem with process compressibility and random error in production/inspection

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第8期52卷 2388-2421页

作者： Karimi-Nasab, M. Sabri-Laghaie, K. Islamic Azad Univ South Tehran Branch Young Researchers & Elite Club Tehran Iran Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

This paper formulates a new imperfect production problem that generates defectives randomly. The production manager conducts total inspections to screen defectives (including reworkable and non-reworkable items) from non-defectives. But, the inspection is error-prone due to different sources. The model determines rates of main and rework processes, batch size and backlog. As the model is a nonlinear programme and it is difficult to obtain an algebraic closed-form solution, three randomised approximation algorithms are developed. Under certain conditions, the algorithms can find the global optimum in polynomial time. Finally numerical analyses are reported.

关键词： imperfect production approximate algorithm inspection process compressibility rework

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EXACT AND approximate algorithmS FOR SCHEDULING NONIDENTICAL PROCESSORS

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JOURNAL OF THE ACM 1976年第2期23卷 317-327页

作者： HOROWITZ, E SAHNI, S UNIV SO CALIF PROGRAM COMP SCI LOS ANGELES CA 90007 USA UNIV MINNESOTA DEPT COMP INFORMATION & CONTROL SCI MINNEAPOLIS MN 55455 USA

Exact and approximate algorithms are presented for scheduling independent tasks in a multiprocessor environment in which the processors have different speeds. Dynamic programming type algorithms are presented which minimize finish time and weighted mean flow time on two processors. The generalization to m processors is direct. These algorithms have a worst-case complexity which is exponential in the number of tasks. Therefore approximation algorithms of low polynomial complexity are also obtained for the above problems. These algorithms are guaranteed to obtain solutions that are close to the optimal. For the case of minimizing mean flow time on m-processors an algorithm is given whose complexity is O(n log mn).

关键词： approximate algorithm complexity exact algorithms finish time mean flow time scheduling independent tasks uniform processors unrelated processors weighted mean flow time

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