Computer Standards & Interfaces最新文献_第3页

SiamIDS: A novel cloud-centric Siamese Bi-LSTM framework for interpretable intrusion detection in large-scale IoT networks SiamIDS：一种新的以云为中心的Siamese Bi-LSTM框架，用于大规模物联网网络中的可解释入侵检测

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-15 DOI: 10.1016/j.csi.2025.104119

Prabu Kaliyaperumal , Palani Latha , Selvaraj Palanisamy , Sridhar Pushpanathan , Anand Nayyar , Balamurugan Balusamy , Ahmad Alkhayyat

The rapid proliferation of Internet of Things (IoT) devices has heightened the need for scalable and interpretable intrusion detection systems (IDS) capable of operating efficiently in cloud-centric environments. Existing IDS approaches often struggle with real-time processing, zero-day attack detection, and model transparency. To address these challenges, this paper proposes SiamIDS, a novel cloud-native framework that integrates contrastive Siamese Bi-directional LSTM (Bi-LSTM) modeling, autoencoder-based dimensionality reduction, SHapley Additive exPlanations (SHAP) for interpretability, and Ordering Points To Identify the Clustering Structure (OPTICS) clustering for unsupervised threat categorization. The framework aims to enhance the detection of both known and previously unseen threats in large-scale IoT networks by learning behavioral similarity across network flows. Trained on the CIC IoT-DIAD 2024 dataset, SiamIDS achieves superior detection performance with an F1-score of 99.45%, recall of 98.96%, and precision of 99.94%. Post-detection OPTICS clustering yields a Silhouette Score of 0.901, DBI of 0.092, and ARI of 0.889, supporting accurate threat grouping. The system processes over 220,000 samples/sec with a RAM usage under 1.5 GB, demonstrating real-time readiness. Compared to state-of-the-art methods, SiamIDS improves F1-score by 2.8% and reduces resource overhead by up to 25%, establishing itself as an accurate, efficient, and explainable IDS for next-generation IoT ecosystems.

物联网（IoT）设备的快速扩散，提高了对可扩展和可解释的入侵检测系统（IDS）的需求，这些系统能够在以云为中心的环境中高效运行。现有的IDS方法经常与实时处理、零日攻击检测和模型透明性作斗争。为了解决这些挑战，本文提出了SiamIDS，这是一种新的云原生框架，它集成了对比Siamese双向LSTM （Bi-LSTM）建模、基于自编码器的降维、SHapley加性解释（SHAP）的可解释性，以及用于无监督威胁分类的排序点识别聚类结构（OPTICS）聚类。该框架旨在通过学习跨网络流的行为相似性来增强对大规模物联网网络中已知和以前未见过的威胁的检测。在CIC IoT-DIAD 2024数据集上训练后，SiamIDS达到了优异的检测性能，f1得分为99.45%，召回率为98.96%，准确率为99.94%。检测后的OPTICS聚类得到的Silhouette Score为0.901，DBI为0.092，ARI为0.889，支持准确的威胁分组。系统每秒处理超过220,000个样本，RAM使用率低于1.5 GB，显示了实时准备。与最先进的方法相比，SiamIDS将f1分数提高了2.8%，并将资源开销降低了25%，使其成为下一代物联网生态系统中准确、高效、可解释的IDS。

{"title":"SiamIDS: A novel cloud-centric Siamese Bi-LSTM framework for interpretable intrusion detection in large-scale IoT networks","authors":"Prabu Kaliyaperumal , Palani Latha , Selvaraj Palanisamy , Sridhar Pushpanathan , Anand Nayyar , Balamurugan Balusamy , Ahmad Alkhayyat","doi":"10.1016/j.csi.2025.104119","DOIUrl":"10.1016/j.csi.2025.104119","url":null,"abstract":"<div><div>The rapid proliferation of Internet of Things (IoT) devices has heightened the need for scalable and interpretable intrusion detection systems (IDS) capable of operating efficiently in cloud-centric environments. Existing IDS approaches often struggle with real-time processing, zero-day attack detection, and model transparency. To address these challenges, this paper proposes SiamIDS, a novel cloud-native framework that integrates contrastive Siamese Bi-directional LSTM (Bi-LSTM) modeling, autoencoder-based dimensionality reduction, SHapley Additive exPlanations (SHAP) for interpretability, and Ordering Points To Identify the Clustering Structure (OPTICS) clustering for unsupervised threat categorization. The framework aims to enhance the detection of both known and previously unseen threats in large-scale IoT networks by learning behavioral similarity across network flows. Trained on the CIC IoT-DIAD 2024 dataset, SiamIDS achieves superior detection performance with an F1-score of 99.45%, recall of 98.96%, and precision of 99.94%. Post-detection OPTICS clustering yields a Silhouette Score of 0.901, DBI of 0.092, and ARI of 0.889, supporting accurate threat grouping. The system processes over 220,000 samples/sec with a RAM usage under 1.5 GB, demonstrating real-time readiness. Compared to state-of-the-art methods, SiamIDS improves F1-score by 2.8% and reduces resource overhead by up to 25%, establishing itself as an accurate, efficient, and explainable IDS for next-generation IoT ecosystems.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104119"},"PeriodicalIF":3.1,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Refining decision boundaries via dynamic label adversarial training for robust traffic classification 基于动态标签对抗训练的鲁棒流量分类决策边界优化

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-13 DOI: 10.1016/j.csi.2025.104111

Haoyu Tong , Meixia Miao , Yundong Liu , Xiaoyu Zhang , Xiangyang Luo , Willy Susilo

Network traffic classification plays a critical role in securing modern communication systems, as it enables the identification of malicious or abnormal patterns within traffic data. With the growing complexity of network environments, deep learning models have emerged as a compelling solution due to their ability to automatically learn discriminative representations from raw traffic. However, these models are highly vulnerable to adversarial examples, which can significantly degrade their performance by introducing imperceptible perturbations. While adversarial training (AT) has emerged as a primary defense, it often suffers from label noise, particularly when hard labels are forcibly assigned to adversarial examples whose true class may be ambiguous. In this work, we first analyze the detrimental effect of label noise on adversarial training, revealing that forcing hard labels onto adversarial examples can cause excessive shifts of the decision boundary away from the adversarial examples, which in turn degrades the model’s generalization. Motivated by the theoretical analysis, we propose Dynamic Label Adversarial Training (DLAT), a novel AT framework that mitigates label noise via dynamically mixed soft labels. DLAT interpolates the logits of clean and adversarial examples to estimate the labels of boundary-adjacent examples, which are then used as soft labels for adversarial examples. By adaptively aligning the decision boundary toward the vicinity of adversarial examples, the framework constrains unnecessary boundary shifts and alleviates generalization degradation caused by label noise. Extensive evaluations on network traffic classification benchmarks validate the effectiveness of DLAT in outperforming standard adversarial training and its variants in both robustness and generalization.

网络流分类在确保现代通信系统的安全方面起着至关重要的作用，因为它可以识别流量数据中的恶意或异常模式。随着网络环境的日益复杂，深度学习模型已经成为一个令人信服的解决方案，因为它们能够从原始流量中自动学习判别表示。然而，这些模型非常容易受到对抗性示例的影响，对抗性示例可以通过引入难以察觉的扰动来显着降低其性能。虽然对抗性训练（AT）已经成为一种主要的防御手段，但它经常受到标签噪音的影响，特别是当硬标签被强制分配给真实类别可能不明确的对抗性示例时。在这项工作中，我们首先分析了标签噪声对对抗训练的有害影响，揭示了将硬标签强加到对抗样本上可能会导致决策边界过度偏离对抗样本，从而降低模型的泛化能力。在理论分析的启发下，我们提出了动态标签对抗训练（Dynamic Label Adversarial Training， DLAT），这是一种通过动态混合软标签来减轻标签噪声的新型标签对抗训练框架。DLAT插值干净和对抗示例的逻辑来估计边界相邻示例的标签，然后将其用作对抗示例的软标签。通过自适应地将决策边界对齐到对抗样本附近，该框架限制了不必要的边界移动，减轻了由标签噪声引起的泛化退化。对网络流量分类基准的广泛评估验证了DLAT在鲁棒性和泛化方面优于标准对抗性训练及其变体的有效性。

{"title":"Refining decision boundaries via dynamic label adversarial training for robust traffic classification","authors":"Haoyu Tong , Meixia Miao , Yundong Liu , Xiaoyu Zhang , Xiangyang Luo , Willy Susilo","doi":"10.1016/j.csi.2025.104111","DOIUrl":"10.1016/j.csi.2025.104111","url":null,"abstract":"<div><div>Network traffic classification plays a critical role in securing modern communication systems, as it enables the identification of malicious or abnormal patterns within traffic data. With the growing complexity of network environments, deep learning models have emerged as a compelling solution due to their ability to automatically learn discriminative representations from raw traffic. However, these models are highly vulnerable to adversarial examples, which can significantly degrade their performance by introducing imperceptible perturbations. While adversarial training (AT) has emerged as a primary defense, it often suffers from label noise, particularly when hard labels are forcibly assigned to adversarial examples whose true class may be ambiguous. In this work, we first analyze the detrimental effect of label noise on adversarial training, revealing that forcing hard labels onto adversarial examples can cause excessive shifts of the decision boundary away from the adversarial examples, which in turn degrades the model’s generalization. Motivated by the theoretical analysis, we propose Dynamic Label Adversarial Training (DLAT), a novel AT framework that mitigates label noise via dynamically mixed soft labels. DLAT interpolates the logits of clean and adversarial examples to estimate the labels of boundary-adjacent examples, which are then used as soft labels for adversarial examples. By adaptively aligning the decision boundary toward the vicinity of adversarial examples, the framework constrains unnecessary boundary shifts and alleviates generalization degradation caused by label noise. Extensive evaluations on network traffic classification benchmarks validate the effectiveness of DLAT in outperforming standard adversarial training and its variants in both robustness and generalization.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104111"},"PeriodicalIF":3.1,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and secure multi-user kNN queries with dynamic POIs updating 具有动态poi更新的高效安全的多用户kNN查询

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-11 DOI: 10.1016/j.csi.2025.104112

Yining Jia , Yali Liu , Congai Zeng , Xujie Ding , Jianting Ning

The

k

-nearest neighbors (

k

NN) query is a key operation in spatial and multimedia databases, which is widely applied in fields such as electronic healthcare and Location-Based Services (LBS). With the rapid development of cloud computing, uploading private data of Data Owner (DO) to Cloud Servers (CS) has become a trend. However, existing

k

NN queries schemes are not designed for multi-user environments, cannot timely update the points of interest (POIs) stored in CS, and suffer from low query efficiency. Therefore, this paper proposes efficient and secure multi-user

k

NN queries with dynamic POIs updating, named DESM

k

NN, which achieves secure multi-user

k

NN queries. To improve query efficiency, DESM

k

NN adopts a two-stage search framework, which consists of an initial filtering stage based on hierarchical clustering to effectively constrain the search range, followed by a more efficient precise search stage. Based on this framework, DESM

k

NN designs a set of security protocols for efficient query processing and enables dynamic POIs updates. Meanwhile, DESM

k

NN not only utilizes Distributed Two Trapdoors Public-Key Cryptosystem (DT-PKC) to enable multi-user queries but also ensures data privacy, query privacy, result privacy and access pattern privacy. Moreover, DESM

k

NN can verify the correctness and completeness of queries results. Finally, security analysis proves that DESM

k

NN meets the formal security definition of multiparty computation, and experimental evaluation shows that DESM

k

NN improves query efficiency by up to 45.5% compared with existing

k

NN queries scheme.

kNN查询是空间和多媒体数据库中的一项关键操作，广泛应用于电子医疗保健和基于位置的服务（LBS）等领域。随着云计算的快速发展，将数据所有者（data Owner， DO）的私有数据上传到云服务器（cloud Servers， CS）已成为一种趋势。然而，现有的kNN查询方案不是针对多用户环境设计的，不能及时更新存储在CS中的兴趣点（poi），并且查询效率较低。为此，本文提出了一种高效、安全且具有动态poi更新的多用户kNN查询方法DESMkNN，实现了安全的多用户kNN查询。为了提高查询效率，DESMkNN采用了两阶段搜索框架，即基于分层聚类的初始过滤阶段有效约束搜索范围，然后是更高效的精确搜索阶段。基于这个框架，DESMkNN设计了一组安全协议，用于高效的查询处理，并支持动态poi更新。同时，DESMkNN不仅利用Distributed Two Trapdoors Public-Key Cryptosystem （DT-PKC）实现多用户查询，还保证了数据隐私、查询隐私、结果隐私和访问模式隐私。此外，DESMkNN可以验证查询结果的正确性和完整性。最后，安全性分析证明DESMkNN符合多方计算的正式安全定义，实验评估表明，与现有的kNN查询方案相比，DESMkNN查询效率提高了45.5%。

{"title":"Efficient and secure multi-user kNN queries with dynamic POIs updating","authors":"Yining Jia , Yali Liu , Congai Zeng , Xujie Ding , Jianting Ning","doi":"10.1016/j.csi.2025.104112","DOIUrl":"10.1016/j.csi.2025.104112","url":null,"abstract":"<div><div>The <span><math><mi>k</mi></math></span>-nearest neighbors (<span><math><mi>k</mi></math></span>NN) query is a key operation in spatial and multimedia databases, which is widely applied in fields such as electronic healthcare and Location-Based Services (LBS). With the rapid development of cloud computing, uploading private data of Data Owner (DO) to Cloud Servers (CS) has become a trend. However, existing <span><math><mi>k</mi></math></span>NN queries schemes are not designed for multi-user environments, cannot timely update the points of interest (POIs) stored in CS, and suffer from low query efficiency. Therefore, this paper proposes efficient and secure multi-user <span><math><mi>k</mi></math></span>NN queries with dynamic POIs updating, named DESM<span><math><mi>k</mi></math></span>NN, which achieves secure multi-user <span><math><mi>k</mi></math></span>NN queries. To improve query efficiency, DESM<span><math><mi>k</mi></math></span>NN adopts a two-stage search framework, which consists of an initial filtering stage based on hierarchical clustering to effectively constrain the search range, followed by a more efficient precise search stage. Based on this framework, DESM<span><math><mi>k</mi></math></span>NN designs a set of security protocols for efficient query processing and enables dynamic POIs updates. Meanwhile, DESM<span><math><mi>k</mi></math></span>NN not only utilizes Distributed Two Trapdoors Public-Key Cryptosystem (DT-PKC) to enable multi-user queries but also ensures data privacy, query privacy, result privacy and access pattern privacy. Moreover, DESM<span><math><mi>k</mi></math></span>NN can verify the correctness and completeness of queries results. Finally, security analysis proves that DESM<span><math><mi>k</mi></math></span>NN meets the formal security definition of multiparty computation, and experimental evaluation shows that DESM<span><math><mi>k</mi></math></span>NN improves query efficiency by up to 45.5% compared with existing <span><math><mi>k</mi></math></span>NN queries scheme.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104112"},"PeriodicalIF":3.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Co-distillation-based defense framework for federated knowledge graph embedding against poisoning attacks 基于协同蒸馏的联邦知识图嵌入中毒攻击防御框架

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-09 DOI: 10.1016/j.csi.2025.104113

Yiqin Lu, Jiarui Chen, Jiancheng Qin

Federated knowledge graph embedding (FKGE) enables collaborative knowledge sharing without data exchange, but it also introduces risks of poisoning attacks that degrade model accuracy or force incorrect outputs. Protecting FKGE from poisoning attacks becomes a critical research problem. This paper reveals the malicious strategy of untargeted FKGE poisoning attacks and proposes CoDFKGE, a co-distillation-based FKGE framework for defending against poisoning attacks. CoDFKGE deploys two collaborative knowledge graph embedding models on clients, decoupling prediction parameters from shared parameters as a model-agnostic solution. By designing distinct distillation loss functions, CoDFKGE transfers clean knowledge from potentially poisoned shared parameters while compressing dimensions to reduce communication overhead. Experiments show CoDFKGE preserves link prediction performance with lower communication costs, eliminates malicious manipulations under targeted poisoning attacks, and significantly mitigates accuracy degradation under untargeted poisoning attacks.

联邦知识图嵌入（FKGE）可以在没有数据交换的情况下实现协作知识共享，但它也引入了中毒攻击的风险，这种攻击会降低模型的准确性或强制输出错误。保护FKGE免受投毒攻击成为关键的研究问题。本文揭示了非目标FKGE投毒攻击的恶意策略，提出了基于共蒸馏的FKGE框架CoDFKGE来防御投毒攻击。CoDFKGE在客户端部署了两个协作知识图嵌入模型，将预测参数与共享参数解耦，作为模型不可知的解决方案。通过设计不同的蒸馏损失函数，CoDFKGE从潜在的有毒共享参数中转移干净的知识，同时压缩维度以减少通信开销。实验表明，CoDFKGE在保持链路预测性能的同时降低了通信成本，消除了针对性投毒攻击下的恶意操作，并显著减轻了非针对性投毒攻击下的精度下降。

引用次数: 0

Robust zero-watermarking method for multi-medical images based on Chebyshev–Fourier moments and Contourlet-FFT 基于chebyhev - fourier矩和Contourlet-FFT的多医学图像鲁棒零水印方法

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-08 DOI: 10.1016/j.csi.2025.104115

Xinhui Lu , Guangyun Yang , Yu Lu , Xiangguang Xiong

Classical robust watermarking methods embed secret data into a cover image designed to protect its copyright. However, they suffer from the problem of balancing imperceptibility and robustness. To address this issue, the impact of conventional attacks on the stability of feature vectors extracted from the cover image is examined. Accordingly, we proposed a zero-watermarking method with high attack resistance for multi-medical images by employing Contourlet transform (CT), Chebyshev–Fourier moments (CHFMs), and fast Fourier transform (FFT). First, each medical image is normalized separately, and the normalized images are fused using a dual-tree complex wavelet transform-based method. Second, the effective region is extracted and subjected to the CT. The CHFMs of the low-frequency sub-bands are calculated, and the FFT is performed on the generated amplitude sequence to construct a feature matrix. A feature image is generated by combining the magnitude of each feature value with the overall mean. Finally, the copyrighted image is encrypted using the Lorenz chaotic system and Fibonacci Q-matrix, after which an exclusive-OR operation is applied between the generated feature image and the encrypted copyrighted image to produce a zero-watermarking signal. The results show that the proposed method exhibits excellent resistance to attack with a normalized correlation coefficient of up to 0.994 between the extracted image and the original copyrighted one. Furthermore, the average anti-attack performance of our proposed method is approximately 2% higher compared to similar existing methods, indicating that our proposed method is highly resistant to conventional, geometric, and combinatorial attacks.

经典的鲁棒水印方法将秘密数据嵌入封面图像中，以保护其版权。然而，它们面临着平衡不可感知性和鲁棒性的问题。为了解决这个问题，研究了传统攻击对从封面图像中提取的特征向量稳定性的影响。据此，我们提出了一种基于Contourlet变换（CT）、chebyshef - Fourier矩（CHFMs）和快速傅里叶变换（FFT）的抗攻击多医学图像零水印方法。首先，对每张医学图像分别进行归一化处理，并采用基于双树复小波变换的方法对归一化后的图像进行融合。其次，提取有效区域并进行CT处理；计算低频子带的CHFMs，对生成的幅值序列进行FFT，构造特征矩阵。将每个特征值的大小与整体均值相结合，生成特征图像。最后，利用Lorenz混沌系统和Fibonacci q矩阵对版权图像进行加密，然后在生成的特征图像与加密后的版权图像之间进行异或运算，产生零水印信号。结果表明，该方法具有良好的抗攻击性能，提取的图像与原始版权图像的归一化相关系数高达0.994。此外，与现有的类似方法相比，我们提出的方法的平均抗攻击性能提高了约2%，这表明我们提出的方法对传统攻击、几何攻击和组合攻击具有很高的抵抗力。

{"title":"Robust zero-watermarking method for multi-medical images based on Chebyshev–Fourier moments and Contourlet-FFT","authors":"Xinhui Lu , Guangyun Yang , Yu Lu , Xiangguang Xiong","doi":"10.1016/j.csi.2025.104115","DOIUrl":"10.1016/j.csi.2025.104115","url":null,"abstract":"<div><div>Classical robust watermarking methods embed secret data into a cover image designed to protect its copyright. However, they suffer from the problem of balancing imperceptibility and robustness. To address this issue, the impact of conventional attacks on the stability of feature vectors extracted from the cover image is examined. Accordingly, we proposed a zero-watermarking method with high attack resistance for multi-medical images by employing Contourlet transform (CT), Chebyshev–Fourier moments (CHFMs), and fast Fourier transform (FFT). First, each medical image is normalized separately, and the normalized images are fused using a dual-tree complex wavelet transform-based method. Second, the effective region is extracted and subjected to the CT. The CHFMs of the low-frequency sub-bands are calculated, and the FFT is performed on the generated amplitude sequence to construct a feature matrix. A feature image is generated by combining the magnitude of each feature value with the overall mean. Finally, the copyrighted image is encrypted using the Lorenz chaotic system and Fibonacci Q-matrix, after which an exclusive-OR operation is applied between the generated feature image and the encrypted copyrighted image to produce a zero-watermarking signal. The results show that the proposed method exhibits excellent resistance to attack with a normalized correlation coefficient of up to 0.994 between the extracted image and the original copyrighted one. Furthermore, the average anti-attack performance of our proposed method is approximately 2% higher compared to similar existing methods, indicating that our proposed method is highly resistant to conventional, geometric, and combinatorial attacks.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104115"},"PeriodicalIF":3.1,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel hybrid WOA–GWO algorithm for multi-objective optimization of energy efficiency and reliability in heterogeneous computing 一种新的混合WOA-GWO算法用于异构计算中能效和可靠性的多目标优化

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-07 DOI: 10.1016/j.csi.2025.104106

Karishma, Harendra Kumar

Heterogeneous computing systems are widely adopted for their capacity to optimize performance and energy efficiency across diverse computational environments. However, most existing task scheduling techniques address either energy reduction or reliability enhancement, rarely achieving both simultaneously. This study proposes a novel hybrid whale optimization algorithm–grey wolf optimizer (WOA–GWO) integrated with dynamic voltage and frequency scaling (DVFS) and an insert-reversed block operation to overcome this dual challenge. The proposed Hybrid WOA–GWO (HWWO) framework enhances task prioritization using the dynamic variant rank heterogeneous earliest-finish-time (DVR-HEFT) approach to ensure efficient processor allocation and reduced computation time. The algorithm’s performance was evaluated on real-world constrained optimization problems from CEC 2020, as well as Fast Fourier Transform (FFT) and Gaussian Elimination (GE) applications. Experimental results demonstrate that HWWO achieves substantial gains in both energy efficiency and reliability, reducing total energy consumption by 55% (from 170.52 to 75.67 units) while increasing system reliability from 0.8804 to 0.9785 compared to state-of-the-art methods such as SASS, EnMODE, sCMAgES, and COLSHADE. The experimental results, implemented on varying tasks and processor counts, further demonstrate that the proposed algorithmic approach outperforms existing state-of-the-art and metaheuristic algorithms by delivering superior energy efficiency, maximizing reliability, minimizing computation time, reducing schedule length ratio (SLR), optimizing the communication-to-computation ratio (CCR), enhancing resource utilization, and minimizing sensitivity analysis. These findings confirm that the proposed model effectively bridges the existing research gap by providing a robust, energy-aware, and reliability-optimized scheduling framework for heterogeneous computing environments.

异构计算系统因其在不同计算环境中优化性能和能源效率的能力而被广泛采用。然而，大多数现有的任务调度技术要么解决能耗降低问题，要么解决可靠性提高问题，很少同时实现这两个目标。为了克服这一双重挑战，本研究提出了一种新的混合鲸鱼优化算法-灰狼优化器（WOA-GWO），该算法集成了动态电压和频率缩放（DVFS）和插入反转块操作。提出的混合WOA-GWO （HWWO）框架利用动态可变等级异构最早完成时间（DVR-HEFT）方法增强任务优先级，以确保高效的处理器分配和减少计算时间。该算法的性能在CEC 2020的实际约束优化问题以及快速傅里叶变换（FFT）和高斯消去（GE）应用中进行了评估。实验结果表明，与SASS、EnMODE、sCMAgES和COLSHADE等最先进的方法相比，HWWO在能源效率和可靠性方面都取得了巨大的进步，将总能耗降低了55%（从170.52单位降低到75.67单位），同时将系统可靠性从0.8804提高到0.9785。在不同任务和处理器数量上的实验结果进一步表明，该算法通过提供卓越的能效、最大限度的可靠性、最小的计算时间、降低调度长度比（SLR）、优化通信与计算比（CCR）、提高资源利用率和最小化灵敏度分析，优于现有的最先进的和元启发式算法。这些发现证实了所提出的模型通过为异构计算环境提供鲁棒性、能量感知和可靠性优化的调度框架，有效地弥补了现有的研究差距。

{"title":"A novel hybrid WOA–GWO algorithm for multi-objective optimization of energy efficiency and reliability in heterogeneous computing","authors":"Karishma, Harendra Kumar","doi":"10.1016/j.csi.2025.104106","DOIUrl":"10.1016/j.csi.2025.104106","url":null,"abstract":"<div><div>Heterogeneous computing systems are widely adopted for their capacity to optimize performance and energy efficiency across diverse computational environments. However, most existing task scheduling techniques address either energy reduction or reliability enhancement, rarely achieving both simultaneously. This study proposes a novel hybrid whale optimization algorithm–grey wolf optimizer (WOA–GWO) integrated with dynamic voltage and frequency scaling (DVFS) and an insert-reversed block operation to overcome this dual challenge. The proposed Hybrid WOA–GWO (HWWO) framework enhances task prioritization using the dynamic variant rank heterogeneous earliest-finish-time (DVR-HEFT) approach to ensure efficient processor allocation and reduced computation time. The algorithm’s performance was evaluated on real-world constrained optimization problems from CEC 2020, as well as Fast Fourier Transform (FFT) and Gaussian Elimination (GE) applications. Experimental results demonstrate that HWWO achieves substantial gains in both energy efficiency and reliability, reducing total energy consumption by 55% (from 170.52 to 75.67 units) while increasing system reliability from 0.8804 to 0.9785 compared to state-of-the-art methods such as SASS, EnMODE, sCMAgES, and COLSHADE. The experimental results, implemented on varying tasks and processor counts, further demonstrate that the proposed algorithmic approach outperforms existing state-of-the-art and metaheuristic algorithms by delivering superior energy efficiency, maximizing reliability, minimizing computation time, reducing schedule length ratio (SLR), optimizing the communication-to-computation ratio (CCR), enhancing resource utilization, and minimizing sensitivity analysis. These findings confirm that the proposed model effectively bridges the existing research gap by providing a robust, energy-aware, and reliability-optimized scheduling framework for heterogeneous computing environments.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104106"},"PeriodicalIF":3.1,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quality assessment for software data validation in automotive industry: A systematic literature review 汽车工业软件数据验证的质量评估：系统的文献综述

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-04 DOI: 10.1016/j.csi.2025.104110

Gilmar Pagoto , Luiz Eduardo Galvão Martins , Jefferson Seide Molléri

Context

The complexity of automotive systems continues to grow, making software quality assessment crucial for vehicle performance, safety, and cybersecurity.

Objectives

This study explores Quality Assessment (QA) in this context, focusing on its key characteristics, practical implications, and expected deliverables.

Method

We performed a systematic literature review (SLR) by selecting 60 studies from digital libraries.

Results

This SLR highlighted essential QA characteristics that should be incorporated into a software validation phase. Our insights encourage the exploration of advanced techniques, such as Artificial Intelligence (AI), and Machine Learning (ML), to support safety-critical software quality assessments in the automotive domain.

Conclusion

The QA of software data validation requires a holistic approach that combines safety, security, and customer expectations, aligned with industry standards, requirements, and specifications. The relevance of AI and ML in managing complex technologies is evidenced, and the traditional real-world validation dependencies bring risks for safety-critical systems validation.

汽车系统的复杂性持续增长，使得软件质量评估对车辆性能、安全和网络安全至关重要。本研究在此背景下探讨了质量评估（QA），重点关注其关键特征、实际意义和预期可交付成果。方法从数字图书馆中选取60篇文献进行系统文献回顾（SLR）。结果：该单反突出了应纳入软件验证阶段的基本QA特征。我们的见解鼓励探索先进技术，如人工智能（AI）和机器学习（ML），以支持汽车领域安全关键软件质量评估。软件数据验证的QA需要一个整体的方法，将安全、保障和客户期望结合起来，并与行业标准、需求和规范保持一致。人工智能和机器学习在管理复杂技术方面的相关性得到了证明，传统的现实世界验证依赖关系为安全关键系统验证带来了风险。

{"title":"Quality assessment for software data validation in automotive industry: A systematic literature review","authors":"Gilmar Pagoto , Luiz Eduardo Galvão Martins , Jefferson Seide Molléri","doi":"10.1016/j.csi.2025.104110","DOIUrl":"10.1016/j.csi.2025.104110","url":null,"abstract":"<div><h3>Context</h3><div>The complexity of automotive systems continues to grow, making software quality assessment crucial for vehicle performance, safety, and cybersecurity.</div></div><div><h3>Objectives</h3><div>This study explores Quality Assessment (QA) in this context, focusing on its key characteristics, practical implications, and expected deliverables.</div></div><div><h3>Method</h3><div>We performed a systematic literature review (SLR) by selecting 60 studies from digital libraries.</div></div><div><h3>Results</h3><div>This SLR highlighted essential QA characteristics that should be incorporated into a software validation phase. Our insights encourage the exploration of advanced techniques, such as Artificial Intelligence (AI), and Machine Learning (ML), to support safety-critical software quality assessments in the automotive domain.</div></div><div><h3>Conclusion</h3><div>The QA of software data validation requires a holistic approach that combines safety, security, and customer expectations, aligned with industry standards, requirements, and specifications. The relevance of AI and ML in managing complex technologies is evidenced, and the traditional real-world validation dependencies bring risks for safety-critical systems validation.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104110"},"PeriodicalIF":3.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A requirement-driven method for process mining based on model-driven engineering 基于模型驱动工程的需求驱动过程挖掘方法

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-04 DOI: 10.1016/j.csi.2025.104108

Selsabil Ines Bouhidel , Mohammed Mounir Bouhamed , Gregorio Diaz , Nabil Belala

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information.

流程挖掘使用事件日志分析业务流程。现有的工具生成模型来促进此任务并改进原始业务流程，但是由于所获得的模型的复杂性，结果往往不令人满意。在此上下文中面临的挑战中，我们确定了与特定业务需求的不一致，从而阻止了管理人员访问关键数据并做出有效决策。在本文中，我们提出了一种以元建模为中心的需求驱动方法，它可以帮助开发专门针对组织需求的过程挖掘工具。因此，我们引入了一种需求驱动的方法来解决模型与所需信息不一致的关键挑战。该方法采用模型驱动工程来简化过程挖掘结果的表述、分析和解释。所提出的方法是迭代的，涉及几个步骤。首先，服务管理器定义一个特定的业务问题。其次，服务管理人员和开发人员协作建立表示目标数据的元模型。第三，开发人员使用适当的分析技术提取相关数据并将其可视化。最后，服务经理和开发人员共同解释这些可视化，以告知战略决策。这种需求驱动的方法使开发人员能够专注于相关信息。与通用框架（例如，ProM、Disco）不同，该方法强调专一性、迭代细化和密切涉众协作。通过集中建模和过滤无关数据来减少认知超载，采用这种方法的组织可以实现对业务问题更快的响应时间，并开发专门的内部分析工具。因此，这种需求驱动的方法提高了过程挖掘和跨相关分析领域的决策能力。我们通过从沃尔沃集团拥有的文献中提取的真实业务流程来说明我们的方法。我们使用几个过程挖掘的例子来说明所提出的方法与现有工具相比的好处，这些工具无法提供所需的信息。

{"title":"A requirement-driven method for process mining based on model-driven engineering","authors":"Selsabil Ines Bouhidel , Mohammed Mounir Bouhamed , Gregorio Diaz , Nabil Belala","doi":"10.1016/j.csi.2025.104108","DOIUrl":"10.1016/j.csi.2025.104108","url":null,"abstract":"<div><div>Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104108"},"PeriodicalIF":3.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MExpm: Fair computation offloading for batch modular exponentiation with improved privacy and checkability in IoV MExpm: IoV中批量模块化幂运算的公平计算卸载，改进了隐私性和可检查性

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-03 DOI: 10.1016/j.csi.2025.104107

Sipeng Shen , Qiang Wang , Fucai Zhou, Jian Xu, Mingxing Jin

Modular exponentiation is a fundamental cryptographic operation extensively applied in the Internet of Vehicles (IoV). However, its computational intensity imposes significant resource and time demands on intelligent vehicles. Offloading such computations to Mobile Edge Computing (MEC) servers has emerged as a promising approach. Nonetheless, existing schemes are generally impractical, as they either fail to ensure fairness between intelligent vehicles and MEC servers, lack privacy protection for the bases and exponents, or cannot guarantee the correctness of results with overwhelming probability due to potential misbehavior by MEC servers. To address these limitations, we propose MExpm, a fair and efficient computation offloading scheme for batch modular exponentiation under a single untrusted server model. Our scheme leverages blockchain technology to ensure fairness through publicly verifiable results. Furthermore, MExpm achieves high checkability, offering a near-perfect probability of checkability. To enhance privacy, we introduce secure obfuscation and logical split techniques, effectively protecting both the bases and the exponents. Extensive theoretical analysis and experimental results demonstrate that our scheme is not only efficient in terms of computation, communication, and storage overheads but also significantly improves privacy protection and checkability.

模幂运算是一种广泛应用于车联网（IoV）的基本加密运算。然而，其计算强度给智能汽车带来了巨大的资源和时间需求。将此类计算卸载到移动边缘计算（MEC）服务器已成为一种有前途的方法。然而，现有的方案要么无法保证智能车辆与MEC服务器之间的公平性，要么缺乏对基数和指数的隐私保护，要么由于MEC服务器可能存在的不当行为，无法保证结果的绝大多数概率的正确性，这些都是不切实际的。为了解决这些限制，我们提出了MExpm，这是一个公平有效的计算卸载方案，用于在单个不受信任的服务器模型下进行批量模块化幂运算。我们的方案利用区块链技术，通过可公开验证的结果来确保公平性。此外，MExpm实现了高可检查性，提供了近乎完美的可检查性概率。为了增强隐私性，我们引入了安全混淆和逻辑分割技术，有效地保护了基数和指数。大量的理论分析和实验结果表明，我们的方案不仅在计算、通信和存储开销方面有效，而且显著提高了隐私保护和可检查性。

{"title":"MExpm: Fair computation offloading for batch modular exponentiation with improved privacy and checkability in IoV","authors":"Sipeng Shen , Qiang Wang , Fucai Zhou, Jian Xu, Mingxing Jin","doi":"10.1016/j.csi.2025.104107","DOIUrl":"10.1016/j.csi.2025.104107","url":null,"abstract":"<div><div>Modular exponentiation is a fundamental cryptographic operation extensively applied in the Internet of Vehicles (IoV). However, its computational intensity imposes significant resource and time demands on intelligent vehicles. Offloading such computations to Mobile Edge Computing (MEC) servers has emerged as a promising approach. Nonetheless, existing schemes are generally impractical, as they either fail to ensure fairness between intelligent vehicles and MEC servers, lack privacy protection for the bases and exponents, or cannot guarantee the correctness of results with overwhelming probability due to potential misbehavior by MEC servers. To address these limitations, we propose MExpm, a fair and efficient computation offloading scheme for batch modular exponentiation under a single untrusted server model. Our scheme leverages blockchain technology to ensure fairness through publicly verifiable results. Furthermore, MExpm achieves high checkability, offering a near-perfect probability of checkability. To enhance privacy, we introduce secure obfuscation and logical split techniques, effectively protecting both the bases and the exponents. Extensive theoretical analysis and experimental results demonstrate that our scheme is not only efficient in terms of computation, communication, and storage overheads but also significantly improves privacy protection and checkability.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104107"},"PeriodicalIF":3.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEA-GAO: A two-stage approach optimal controller placement in software-defined networks using data envelopment analysis DEA-GAO：一种采用数据包络分析的两阶段方法在软件定义网络中优化控制器配置

IF 3.1 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Standards & Interfaces

Pub Date : 2025-12-02 DOI: 10.1016/j.csi.2025.104109

Fatemeh Dashti, Ali Ghaffari, Nahideh Derakhshanfard, Shiva Taghipoureivazi

Software-defined networks (SDN) have demonstrated considerable benefits in various practical domains by decoupling the control plane from the data plane, thus facilitating programmable network management. This paper presents a two-stage approach for solving the problem of controller placement called DEA-GAO. In the first stage, this strategy assumes the SDN network as a graph and using Data Envelopment Analysis (DEA) and relying on graph centrality metrics such as closeness centrality, betweenness centrality, and eigenvector centrality, calculates the efficiency of nodes to determine the optimal locations for deploying controllers. In the second stage, to allocate switches to controllers, the proposed strategy employs the Green Anaconda Optimization algorithm (GAO) to achieve an optimal allocation while considering network parameters such as average delay, load balancing, and reliability. Finally, to assess the efficacy of the proposed methodology, it is juxtaposed with three extant methods utilizing diverse datasets from the Internet Topology Zoo. The experimental findings indicate that the proposed approach significantly surpasses the existing methods, specifically the hybrid RDMCP-PSO algorithm, heuristic CPP algorithm and PSO algorithm in terms of both average delay (8.8 %, 28.8 % and 22.2 % respectively) and controller utilization (1.5 %, 7.3 % and 32 % respectively).

软件定义网络（SDN）通过将控制平面与数据平面解耦，从而促进可编程网络管理，在各种实际领域中显示出相当大的优势。本文提出了一种两阶段的方法来解决被称为DEA-GAO的控制器放置问题。在第一阶段，该策略将SDN网络假设为一个图，并使用数据包络分析（DEA），依靠图中心性指标（如接近中心性、中间中心性和特征向量中心性）计算节点的效率，以确定部署控制器的最佳位置。第二阶段，在考虑平均时延、负载均衡、可靠性等网络参数的情况下，采用绿色蟒蛇优化算法（Green Anaconda Optimization algorithm， GAO）实现交换机到控制器的最优分配。最后，为了评估所提出方法的有效性，将其与利用互联网拓扑动物园不同数据集的三种现有方法并置。实验结果表明，该方法在平均时延（分别为8.8%、28.8%和22.2%）和控制器利用率（分别为1.5%、7.3%和32%）方面均明显优于现有方法，特别是混合RDMCP-PSO算法、启发式CPP算法和PSO算法。

{"title":"DEA-GAO: A two-stage approach optimal controller placement in software-defined networks using data envelopment analysis","authors":"Fatemeh Dashti, Ali Ghaffari, Nahideh Derakhshanfard, Shiva Taghipoureivazi","doi":"10.1016/j.csi.2025.104109","DOIUrl":"10.1016/j.csi.2025.104109","url":null,"abstract":"<div><div>Software-defined networks (SDN) have demonstrated considerable benefits in various practical domains by decoupling the control plane from the data plane, thus facilitating programmable network management. This paper presents a two-stage approach for solving the problem of controller placement called DEA-GAO. In the first stage, this strategy assumes the SDN network as a graph and using Data Envelopment Analysis (DEA) and relying on graph centrality metrics such as closeness centrality, betweenness centrality, and eigenvector centrality, calculates the efficiency of nodes to determine the optimal locations for deploying controllers. In the second stage, to allocate switches to controllers, the proposed strategy employs the Green Anaconda Optimization algorithm (GAO) to achieve an optimal allocation while considering network parameters such as average delay, load balancing, and reliability. Finally, to assess the efficacy of the proposed methodology, it is juxtaposed with three extant methods utilizing diverse datasets from the Internet Topology Zoo. The experimental findings indicate that the proposed approach significantly surpasses the existing methods, specifically the hybrid RDMCP-PSO algorithm, heuristic CPP algorithm and PSO algorithm in terms of both average delay (8.8 %, 28.8 % and 22.2 % respectively) and controller utilization (1.5 %, 7.3 % and 32 % respectively).</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104109"},"PeriodicalIF":3.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0