Concurrency and Computation-Practice & Experience最新文献

{"title":"HoneyFed Adaptive Deception With Federated Learning Strategy for Next-Generation Robust MANET Security","authors":"C. Aparna,&nbsp;S. Radha,&nbsp;C. Aarthi,&nbsp;K. M. Karthick Raghunath","doi":"10.1002/cpe.70100","DOIUrl":"https://doi.org/10.1002/cpe.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>Mobile Ad hoc networks (MANETs) are key for applications in which flexibility and organization are paramount, but the security of such networks entails threats that can exploit the vulnerability of their open architecture, resulting in various attacks. To address such issues, a novel architectural framework is always required. One such framework is introduced, namely, the HoneyFed Secure Architecture (HFSA), which provides the combination of an advanced honey encryption system with federated learning-based decentralized security to improve the security of MANET. Honey encryption, on the other hand, employs adaptive deception techniques to generate plausible decoy data on decryption failure, employs dynamic key management for tamper resistance, and provides perfect authentication through multi-factor methods and zero-knowledge proofs. We found that federated learning offers decentralized model training, where nodes jointly train local models while exchanging progress updates without exposing raw data, enabling 81.4% more detections of emerging threats while preserving data privacy. Using the proposed HFSA approach achieves a 78% protection improvement against attacks and a 71% reduction in unauthorized access. HFSA offers a robust and scalable framework of security that uses continuous learning and adaptation to the vulnerabilities of the MANETs to enhance network resilience.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithm Substitution-Resistant SM9-Based Searchable Encryption With Cryptographic Reverse Firewall for Cloud Storage","authors":"Gaimei Gao,&nbsp;Mingbo Duan,&nbsp;Yaling Xun,&nbsp;Chunxia Liu,&nbsp;Weichao Dang","doi":"10.1002/cpe.70073","DOIUrl":"https://doi.org/10.1002/cpe.70073","url":null,"abstract":"<div>\u0000 \u0000 <p>To protect data privacy in cloud storage, sensitive data is commonly encrypted before being uploaded to a cloud storage server (CSS). This process challenges secure and efficient ciphertext retrieval. Identity-based encryption with keyword search (IBEKS) enables secure ciphertext retrieval but remains vulnerable to internal adversary attacks, particularly algorithm substitution attacks (ASA) in SM9-based searchable encryption schemes. Additionally, existing protocols lack proactive defense mechanisms, making them vulnerable to insider threats that compromise system integrity. To address these challenges, this article proposes SM9-based Searchable Encryption with Cryptographic Reverse Firewall (SM9SE-CRF), a novel scheme designed to resist internal attacks while ensuring efficient ciphertext retrieval. Initially, a re-randomizable IBEKS framework is developed as the foundation for constructing the SM9 searchable encryption scheme, enhancing both security and performance. Furthermore, Cryptographic reverse firewalls are deployed at both the Key Generation Center (KGC) and user ends, which dynamically re-randomize cryptographic parameters to mitigate risks posed by internal adversaries. The SM9SE-CRF scheme is implemented using the JPBC library and evaluated through comprehensive security and performance analyses. Results demonstrate that SM9SE-CRF effectively mitigates offline keyword guessing attacks and ASA threats from malicious insiders. Performance evaluations reveal that at a 128-bit security level, SM9SE-CRF achieves a 93% reduction in runtime compared to existing schemes, with the cryptographic reverse firewall adding merely 1.16% overhead. This minimal computational cost highlights the practical applicability of SM9SE-CRF in privacy-preserving cloud storage systems, particularly in enterprise data sharing, secure outsourced storage, and cloud-based information retrieval applications.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Advancement in Huffman Coding With a Potential for Parallel Decoding","authors":"K. Viswanathan Iyer,&nbsp;Karthick Seshadri,&nbsp;K. Srinivasulu","doi":"10.1002/cpe.70096","DOIUrl":"https://doi.org/10.1002/cpe.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>With examples we provide a minimum theory framework to understand data compression of text files—using Huffman coding—that will also provide a framework in designing experiments involving encoding/decoding. We propose a parallelizable heuristic for the naïve Huffman encoding and decoding which addresses the difficulty in parallelizing the inherently sequential Huffman decoding. While the proposal is amenable to a design of an efficient parallel algorithm for Huffman decoding, it also achieves a better compression ratio in the sense that the fraction of inputs for it works is over 0.83. The results of simulations of the parallel algorithm on a 64-core machine show that the proposed parallel modified Huffman encoding and decoding results in a faster algorithm when compared to the naïve Huffman scheme and the sequential version of the heuristic proposed. Further, the parallel implementation of the proposed encoding and decoding schemes resulted in a mean speed-up of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mfenced>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <msub>\u0000 <mi>log</mi>\u0000 <mfenced>\u0000 <mfrac>\u0000 <mi>n</mi>\u0000 <mi>r</mi>\u0000 </mfrac>\u0000 </mfenced>\u0000 </msub>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation>$$ Oleft(r{log}_{left(frac{n}{r}right)}nright) $$</annotation>\u0000 </semantics></math> and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>O</mi>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>r</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation>$$ O(r) $$</annotation>\u0000 </semantics></math> respectively over the naïve Huffman encoding and decoding when processing an input of size <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>n</mi>\u0000 </mrow>\u0000 <annotation>$$ n $$</annotation>\u0000 </semantics></math> on a multi-core processor with <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 </mrow>\u0000 <annotation>$$ r $$</annotation>\u0000 </semantics></math> cores.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-Term Power Load Prediction Based on Cluster Analysis and Temporal Convolutional Networks of Attention Mechanism","authors":"Shuqi Niu,&nbsp;Zhao Zhang,&nbsp;Hongyan Zhou,&nbsp;Xue-Bo Chen","doi":"10.1002/cpe.70082","DOIUrl":"https://doi.org/10.1002/cpe.70082","url":null,"abstract":"<div>\u0000 \u0000 <p>Short-term power load prediction has become one of the important contents of smart grid management. Accurate power load prediction can provide a safer, more reliable, and more efficient direction for power system operation. This article proposes a short-term power load forecasting method. Mainly based on the improved fuzzy c-means clustering (FCM) algorithm and a temporal convolutional network (TCN) model combined with an attention mechanism (AM). First, to cluster the load data with the same power consumption behavior into one class, a kernel FCM algorithm based on particle swarm optimization is used. Meanwhile, external factors with high correlation are selected as inputs. The Pearson correlation coefficient can be used to measure the degree of correlation between load data and external factors. Second, by analyzing the degree of correlation between external influencing factors and load data, the channel AM and time AM are introduced into the TCN model. Finally, the effectiveness of the proposed method was verified through a real electricity load dataset. The experimental results indicate that this method can accurately predict future changes in power load. Compared with other models, it also has high accuracy and generalization ability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ranking Influential Nodes in Social Networks Based on the Game of Thieves Algorithm","authors":"Minni Jain,&nbsp;Aditya Gupta,&nbsp;Aaryan Arora,&nbsp;Aayush Patel","doi":"10.1002/cpe.70044","DOIUrl":"https://doi.org/10.1002/cpe.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>With advancements in technology, the number of people using various social networks to share information daily has increased exponentially. To spread information to maximum users, there is a need to effectively identify highly influential nodes in social networks. Still, existing centrality measures and methods have drawbacks relating to computational complexity and accuracy. The proposed method considers the Game of Thieves algorithm to rank influential nodes in social networks effectively with lower computational time that outperforms existing closeness centrality, eigenvector, Game Theory, and VoteRank⁺⁺ algorithms. To evaluate the performance of the proposed work, experiments were conducted on synthetic and real-world network datasets. The results were evaluated with the help of the Susceptible-Infected-Recovered (SIR) model and Node Removal Procedure (NRP) evaluation metrics. These results showed that GOT outperforms the existing measures by 12% on average while giving more accurate results than the standard algorithms for large-scale networks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Business Process Anomaly Behavior Detection Method Based on Multiperspective Association Rules","authors":"Gubao Mao,&nbsp;Xianwen Fang,&nbsp;Ke Lu","doi":"10.1002/cpe.70094","DOIUrl":"https://doi.org/10.1002/cpe.70094","url":null,"abstract":"<div>\u0000 \u0000 <p>Unexpected behavior in business process executions can be identified to give industrial internet systems security assurance for reliable operation. Current research primarily employs consistency analysis or outlier detection of data points to recognize aberrant behavior, neglecting the relationship between behavior and data properties. This work presents a multiperspective association rule-based approach for detecting anomalous behavior in industrial processes. Initially, a log transaction table with behavior relationships is constructed by mining behavior associations and related properties from the data Petri net. Subsequently, through the application of context awareness, behavior-attribute-time associations of frequently occurring itemsets are generated, and pruning procedures are used to mine multiperspective behavior rules under attribute associations. This approach facilitates the identification of anomalous behavior by comparing the support between logs and rules. Ultimately, the proposed method is implemented using the pm4py open-source framework, and evaluations are performed on both simulated and real event logs using multiple metrics. Experimental comparison results demonstrate that the proposed anomaly behavior detection method achieves higher performance.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete Gray Wolf Optimizer for Solving Distributed Permutation Flowshop Scheduling Problem","authors":"Shuilin Chen,&nbsp;Jianguo Zheng","doi":"10.1002/cpe.70090","DOIUrl":"https://doi.org/10.1002/cpe.70090","url":null,"abstract":"<div>\u0000 \u0000 <p>Distributed manufacturing has become a mainstream production mode in economic globalization. A discrete gray wolf optimizer (DGWO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) to minimize makespan. First, an extended Nawaz-Enscore-Ham2 (ENEH2) and a randomly generated hybrid initialization method are used to enhance the diversity and ergodicity of the population. Second, a discrete population update mechanism is proposed for the characteristics of the solved problem to balance exploration and exploitation. The variable neighborhood descent search strategy is used to further improve the quality of the solution. Finally, the Wilcoxon signed rank and the Friedman test are used for statistical comparison analysis. To verify the performance of the DGWO, simulation experiments are conducted on different scales of instances and compared with various methods to demonstrate the advantages of the DGWO for solving the DPFSP.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Strategy Enhanced Wolf Pack Algorithm for Three-Dimensional Path Planning of Unmanned Aerial Vehicles","authors":"Qiang Peng,&nbsp;Renjun Zhan,&nbsp;Husheng Wu,&nbsp;Aiai Wang,&nbsp;Yuanda Lai","doi":"10.1002/cpe.70095","DOIUrl":"https://doi.org/10.1002/cpe.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a Multi-strategy Enhanced Wolf Pack Algorithm (MSEWPA) is proposed to address the three-dimensional (3D) path planning problem for unmanned aerial vehicles (UAVs) in complex environments. Initially, a mathematical model for 3D path planning is constructed, comprehensively considering constraints such as UAV operational efficiency, path safety risks, performance limitations, obstacle avoidance requirements, and noise limits in urban functional areas. Subsequently, the design of the MSEWPA algorithm is elaborated in detail, including the utilization of the Good Lattice Point (GLP) theory to optimize population initialization for enhanced global search capability, the integration of selection, crossover, and mutation operations from the Differential Evolution (DE) algorithm to augment the randomness of wandering, the introduction of a behavior transition factor for adaptive behavior adjustment, the incorporation of light propagation phenomena to improve random search capabilities during the running process, and the design of multiple siege strategies to guide the exploration of globally optimal solutions. To validate the robustness of the algorithm, sensitivity analysis is conducted on key parameters to determine their optimal settings, and ablation experiments are performed to verify the effectiveness of each improvement strategy. Experimental results on the CEC-2017 benchmark test functions demonstrate that MSEWPA excels in solving complex optimization problems, achieving rapid convergence to high-quality global optimal solutions. Furthermore, in four path planning problems of varying complexity, MSEWPA outperforms 11 other state-of-the-art metaheuristic optimization algorithms, demonstrating a strong balance between global and local exploration capabilities. This provides an effective solution for UAV 3D path planning.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Routing Protocol Using Fresh Vehicular Traffic Information for VANETs","authors":"Mohamed Lehsaini,&nbsp;Anas Nawfel Saidi,&nbsp;Tawfiq Nebbou,&nbsp;Pascal Lorenz","doi":"10.1002/cpe.70081","DOIUrl":"https://doi.org/10.1002/cpe.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>Vehicular ad hoc networks (VANETs) are very changeable networks due to the highly dynamic movement of their nodes, resulting in frequent link disconnection and variable node density. One of the most challenging issues in VANETs is to propose a suitable routing scheme that is adapted to the characteristics of such a dynamic topology. Position-based routing schemes that are effective in handling dynamic changes in the topology of VANETs are proposed. This article proposes an efficient routing protocol based on a greedy forwarding approach called ERGF. The proposed protocol is a position-based routing protocol that uses fresh vehicular traffic information in the routing process. ERGF consists of two main algorithms: the vehicle traffic freshness dissemination algorithm and the greedy forwarding algorithm. The both algorithms work together to provide fresh, up-to-date information about vehicle traffic, enabling the proposed routing protocol to effectively withstand dynamic changes in VANET network topology. The proposed protocol has been developed over OMNET++ simulator, evaluated and compared with some other protocols. The simulation results showed that the proposed protocol provided better performance in terms of packet delivery rate and end-to-end delay than the EGyTAR and PBRP protocols. The packet delivery ratio of our proposal is approximately 75% and 6% higher than EGyTAR and PBRP, respectively, and the end-to-end delay of our protocol is reduced by 37% and 7%, respectively, compared with EGyTAR and PBRP for most scenarios.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-Based Epilepsy Recognition via Federated Learning With Differential Privacy","authors":"Yuling Luo,&nbsp;Bingxiong Jiang,&nbsp;Sheng Qin,&nbsp;Qiang Fu,&nbsp;Shunsheng Zhang","doi":"10.1002/cpe.70072","DOIUrl":"https://doi.org/10.1002/cpe.70072","url":null,"abstract":"<div>\u0000 \u0000 <p>Epilepsy is a complex chronic brain disorder that can be identified by observing brain signals. In general, the electroencephalogram (EEG) can be used to detect these brain signals. In order to produce a high-quality model, data from numerous patients can be gathered on a central server. However, sending the patient's raw data to the central computer may lead to privacy leakage. To address this problem, this work uses federated learning and differential privacy to train the model jointly. Furthermore, the epilepsy data is unbalanced as seizure only happens for a minority of time in one day, which influences the performance of the model. Thus, this work also uses label-distribution-aware-margin (LDAM) loss to solve this issue. This work is evaluated in intracranial EEG datasets, which consist of two dogs' EEG records. The global model trained jointly with LDAM loss can achieve an accuracy of 96.95%, a sensitivity of 78.9%, a specificity of 96.145%, an F1 score of 70.435%, and a geometric mean of 87.785%. Compared with the other works, the accuracy has improved by about ˜9.31%, while the specificity and the geometric mean have also improved by about ˜10.75% and ˜1.8%, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 9-11","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}