Journal of Network and Computer Applications最新文献

{"title":"A quantum-secure digital signature-based communication protocol for the Internet of Drones (IoD)","authors":"Jithu Vijay V.P.,&nbsp;Shahanas I.N.,&nbsp;Sabu M. Thampi,&nbsp;Aiswarya S. Nair","doi":"10.1016/j.jnca.2025.104398","DOIUrl":"10.1016/j.jnca.2025.104398","url":null,"abstract":"<div><div>The use of drones is rapidly increasing in areas such as surveillance, defense, and emergency services. As a result, ensuring secure communication and proper authentication has become a major concern in the Internet of Drones, where drones must share data and coordinate their actions in real time. One of the biggest challenges in drone networks is maintaining secure and reliable communication between drones. The dynamic and distributed nature of these networks increases the risk of security breaches. Existing systems mostly rely on cryptographic methods like RSA and ECC. These methods will not remain secure in the future because of advancements in quantum computing. These systems also depend on static data storage and centralized credential management, which make them vulnerable to attacks such as impersonation, replay, and man-in-the-middle. To address these issues, we propose a quantum-secure drone-to-drone authentication and secure communication protocol that utilizes Post Quantum Cryptographic (PQC) algorithms such as, Kyber for encryption and Dilithium for digital signatures. Both are lattice-based lightweight cryptographic algorithms that offer strong resistance against quantum attacks. Instead of storing secret data on drones and to prevent cloning, we use Physical Unclonable Functions (PUF) to generate device specific seeds for authentication and key generation during each session. A Hyperledger Fabric Blockchain is used at the Ground Control Station (GCS) to store drone credentials securely and avoid single point failure. We conducted the formal security analysis using the Burrows–Abadi–Needham (BAN) logic for trust validation and the Scyther tool to formally analyze and verify resistance against classical and quantum-era attacks. In addition to formal proofs, informal analysis confirms that the protocol maintains data integrity and authentication even under active network threats. We implemented the protocol using Raspberry Pi drones and a Linux-based GCS. Performance results show a low computation time of 0.08 s for authentication and 0.12 s for secure communication on Raspberry Pi 5, with minimal memory usage and acceptable communication cost suitable for implementation on resource-constrained drones.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104398"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609218","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}

{"title":"Task scheduling of cloud computing system by frilled lizard optimization with time varying expansion mixed function oscillation and horned lizard camouflage strategy","authors":"Hao-Ming Song,&nbsp;Si-Wen Zhang,&nbsp;Jie-Sheng Wang,&nbsp;Cheng Xing,&nbsp;Yu-Feng Sun,&nbsp;Yu-Cai Wang,&nbsp;Xiao-Fei Sui","doi":"10.1016/j.jnca.2025.104386","DOIUrl":"10.1016/j.jnca.2025.104386","url":null,"abstract":"<div><div>With the increasing complexity and scale of cloud computing systems, task scheduling optimization has become critical for improving resource utilization, enhancing service reliability, and reducing overall energy consumption. Traditional swarm intelligence algorithms often struggle to achieve an effective balance between global exploration and local exploitation, leading to premature convergence or sub-optimal solutions, particularly in large-scale and high-dimensional problem scenarios. To address these challenges, this study proposes a Time Varying Mixed Function Frilled Lizard Optimization algorithm (TMCFLO) that incorporates a horned lizard-inspired camouflage strategy to increase population diversity and prevent premature convergence, alongside a novel mixed function oscillation mechanism, combining sine, cosine, power, logarithm, and Gaussian functions, to enhance local search precision and convergence efficiency. A time-varying expansion factor is further introduced to dynamically regulate oscillation amplitude, ensuring adaptive adjustment of search behavior throughout the optimization process. Extensive evaluations on the CEC 2022 benchmark set demonstrate that TMCFLO outperforms classical algorithms, including PSO, ACO, WOA, AOA, POA, ZOA, HO, RLLPSO and IHBA, achieving up to 26 percent improvement in optimization accuracy. In practical cloud computing task scheduling experiments with 1500 and 3000 tasks, TMCFLO achieves the lowest single task energy consumption of 0.2196, the lowest total energy consumption of 658.80, and the highest energy efficiency of 4.5569, confirming its effectiveness, scalability, and energy-efficient superiority for complex cloud scheduling problems.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104386"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528948","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}

{"title":"Design, implementation, and performance evaluation of a high-performance and high-precision NetFlow/IPFIX flow-monitoring system on a P4 hardware switch","authors":"Shie-Yuan Wang ,&nbsp;Tzu-Ching Lin","doi":"10.1016/j.jnca.2025.104385","DOIUrl":"10.1016/j.jnca.2025.104385","url":null,"abstract":"<div><div>High-performance and high-precision flow monitoring is a crucial function for network management, network bandwidth usage accounting and billing, network security, network forensics, and other important tasks. Nowadays, many commercial switches/routers provide either sFlow, NetFlow, or IPFIX scheme for monitoring the flows traversing a network. sFlow is a scheme widely supported by many switches/routers due to its using a sampling-based method, which greatly reduces the CPU processing load on a switch/router and the network bandwidth required to transmit flow data to a remote collector. However, many small flows may go undetected and the estimated flow data (e.g., the packet count and byte count) for detected flows can significantly deviate from their ground truth.</div><div>NetFlow, which is Cisco Systems’ proprietary technology, does not use a sampling-based method by default. Instead, it tries to collect complete and correct flow data for every flow. However, as the link speed and the flow arrival rate continue to increase, NetFlow also provides a sampling-based option to reduce the CPU utilization of the switch/router. Because NetFlow is proprietary, an Internet Engineering Task Force (IETF) working group has defined IPFIX as an open flow information export protocol based on NetFlow Version 9. The requirements for IPFIX are defined in the RFC 3917 standards. Basically, IPFIX is the same as NetFlow Version 9.</div><div>Due to its high demand on the CPU of the switch/router, currently NetFlow is supported only on very high-end switches/routers and its design and implementation on these commercial switches/routers are not published in the literature. In this paper, we design and implement a high-performance and high-precision NetFlow/IPFIX system on a Programming Protocol-independent Packet Processors (P4) hardware switch. Based on a 20 Gbps playback of a packet trace gathered on an Internet backbone link, experimental results show that our novel method significantly outperforms the typical design and implementation method of NetFlow/IPFIX on a P4 hardware switch. For example, for the number of detected flows during the trace period, our method outperforms the typical method by a factor of 5.72. As for the number of flows whose packet and byte counts are correctly counted, our method outperforms the typical method by a factor of 8.57.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104385"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528949","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}

{"title":"Optimal multi-access edge computing system deployment in private 5G networks for multi-story construction sites","authors":"Shi-Yu Zhang ,&nbsp;Chun-Cheng Lin ,&nbsp;Zhen-Yin Annie Chen ,&nbsp;Der-Jiunn Deng","doi":"10.1016/j.jnca.2025.104384","DOIUrl":"10.1016/j.jnca.2025.104384","url":null,"abstract":"<div><div>Driven by the swift progression of smart construction, the number of sensors and smart devices on construction sites has increased dramatically, posing new challenges to data processing and communications. However, conventional cloud computing framework can hardly meet the requirement for processing enormous real-time data from construction sites, while existing approaches to deploying multi-access edge computing (MEC) servers overlooked the energy usage of MEC servers, as well as the unique physical and network security requirements within the multi-story structure of complex construction sites. Therefore, this work presents a mathematical programming model for private 5G network MEC systems on smart construction sites considering installation, connectivity, energy consumption, security maintenance, and cybersecurity; and further solve it with a hybrid metaheuristic approach that combines simplified harmony search (SHS) and variable neighborhood search (VNS) algorithms. The deployment of private 5G network edge computing servers and base stations is recognized as an NP-hard problem, where conventional mathematical models may fall short in finding practical, optimal solutions. Our proposed hybrid algorithm integrates the global search capability of SHS with the local search efficiency of VNS to comprehensively explore the solution space, providing a robust yet implementable method for complex optimization. The efficacy of this approach is validated through experimental evaluations in real-world construction site scenarios, demonstrating notable advantages in solution quality, stability, energy consumption, and overall cost reduction. Results show that the proposed algorithm significantly minimizes costs related to installation, security maintenance, and data protection, fulfilling diverse constraints effectively and making it a promising solution of deploying the MEC systems in private 5G networks for smart construction sites.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104384"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531188","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}

{"title":"SIoV-IDS: SDN-enabled zero-trust framework for explainable intrusion detection in IoVs using Variational Autoencoders and EX-LSTM","authors":"Muddasar Laghari ,&nbsp;Yuanchang Zhong ,&nbsp;Muhammad Junaid Tahir ,&nbsp;Muhammad Adil","doi":"10.1016/j.jnca.2025.104389","DOIUrl":"10.1016/j.jnca.2025.104389","url":null,"abstract":"<div><div>In response to cyber attacks targeting the Internet of Vehicles (IoV) ecosystem, we propose <strong>SIoV-DS</strong>, a secure framework addressing inter-vehicle communication, intra-vehicle networks, and infrastructure threats using a zero-trust approach. Vehicle data is first encoded with a <em>Variational Autoencoder (V-AE)</em> to mitigate inference attacks, then analyzed by an <em>Extended Long Short-Term Memory (EX-LSTM)</em> detector capable of identifying diverse attacks, including Denial of Service (DoS), spoofing, and malware. For interpretability, <em>Shapley Additive Explanations (SHAP)</em> provide insights into EX-LSTM decisions, assisting Security Operations Center (SOC) analysts. SIoV-DS is deployed over a <em>Software-Defined Networking (SDN)</em> architecture to ensure scalability. Evaluations on <em>CIC-IoV2024</em> and <em>Edge-IIoTset2022</em> datasets demonstrate high accuracy (99.78% and 95.01%, respectively), while inference-time analysis confirms feasibility for real-time detection, effectively securing the IoV ecosystem against advanced cyber threats.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104389"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531187","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}

{"title":"CPPA-SKU: Towards efficient conditional privacy-preserving authentication protocol with secret key update in VANET","authors":"Xinyu Fan ,&nbsp;Shiyuan Xu ,&nbsp;Yibo Cao ,&nbsp;Xue Chen ,&nbsp;Yu Chen ,&nbsp;Tianrun Xu","doi":"10.1016/j.jnca.2025.104369","DOIUrl":"10.1016/j.jnca.2025.104369","url":null,"abstract":"<div><div>The rapid development of intelligent transportation systems (ITS) has raised higher requirements for traffic data sharing and collaboration. As an effective solution, vehicular ad-hoc network (VANET) has emerged to support real-time data transfer between vehicles and infrastructure. However, VANET faces the challenges of data security and privacy. To alleviate these, many conditional privacy-preserving authentication (CPPA) schemes have been proposed. CPPA utilizes signature technology to ensure message authenticity while enabling the effective tracing of malicious vehicles. Unfortunately, traditional CPPA schemes fail to consider the security of secret keys stored in tamper-proof devices (TPDs). Additionally, most existing schemes still suffer from excessive computational and communication overhead. In this paper, we propose CPPA-SKU, an efficient CPPA scheme with message recovery for VANET. CPPA-SKU introduces a secret key update method using a secure pseudo-random function and Shamir’s secret sharing to prevent key leakage issues in TPDs. Additionally, CPPA-SKU enables the recovery of relevant messages, eliminating the need to embed messages in signatures, thereby reducing the communication overhead. Furthermore, CPPA-SKU is implemented based on the elliptic curve cryptosystem, which avoids expensive bilinear pairing operations while ensuring the security of signatures. We also formally prove the security of CPPA-SKU in the random oracle model. Comprehensive performance evaluations indicate that CPPA-SKU reduces computational overhead by approximately 1.3<span><math><mo>×</mo></math></span>–2.8<span><math><mo>×</mo></math></span> and communication overhead by approximately 1.5<span><math><mo>×</mo></math></span>-3.5<span><math><mo>×</mo></math></span>.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104369"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145404577","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}

{"title":"A blockchain-assisted lightweight authentication scheme for smart home environments","authors":"Xiujun Wang,&nbsp;Wenlong Dong,&nbsp;Wenjie Hu,&nbsp;Juyan Li","doi":"10.1016/j.jnca.2025.104397","DOIUrl":"10.1016/j.jnca.2025.104397","url":null,"abstract":"<div><div>With the rapid development of Internet of Things (IoT) technology, smart home systems have significantly enhanced the convenience and automation level of users’ daily lives. However, as sensitive data is transmitted between smart devices over open channels, the security risks associated with data transmission have become increasingly prominent. Authentication and key exchange (AKE) protocols are designed to facilitate identity authentication and confidential communication between smart devices. However, existing AKE protocols often suffer from low efficiency and poor scalability. These limitations make them unsuitable for resource-constrained IoT devices and unable to provide secure mutual authentication. To tackle these challenges, this study introduces a blockchain-assisted lightweight authentication scheme for smart homes. The proposed scheme integrates biometric authentication and device credentials to achieve multi-factor authentication. Meanwhile, blockchain technology is employed to record and protect interactions between users and smart devices, thereby enhancing the security, transparency, and auditability of the communication process. Formal security analysis under the Random Oracle Model (ROM) confirms the scheme’s key confidentiality. Furthermore, informal analysis demonstrates its robustness against common threats, including replay, man-in-the-middle, impersonation, and device capture attacks. Benchmarks against existing protocols demonstrate that our design incurs the least computational, communication, and energy overhead. It achieves this efficiency while preserving robust security and scalability, making it ideal for resource-limited smart-home devices.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104397"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593070","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}

{"title":"Networks of splicing processors: Wheel graph topology simulation","authors":"José Angel Sánchez Martín ,&nbsp;Victor Mitrana ,&nbsp;Mihaela Păun ,&nbsp;José Ramó n Sánchez Couso","doi":"10.1016/j.jnca.2025.104393","DOIUrl":"10.1016/j.jnca.2025.104393","url":null,"abstract":"<div><div>We continue the investigation regarding the simulation of different network topologies in networks having processors inspired by the DNA splicing which are located in their nodes. These networks are called networks of splicing processors. So far, it was shown how every network of splicing processors, no matter its topology, can be converted, by a direct construction, into an equivalent network with a desired topology, especially a common one like star, grid or complete (or full-mesh). A short discussion highlights the importance of wheel graph topology in relation to biology and DNA computing. This work completes this study by giving an effective construction of a wheel (ring-star) network of splicing processors that is equivalent to an arbitrary network. The size and time complexity of our construction is evaluated. Finally, we discuss a very preliminary simulation of the networks considered here by means of recent technologies and strategies that are suitable to handle the massive data and parallel processing requirements of these networks.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104393"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593078","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}

{"title":"Data evacuation optimization using multi-objective reinforcement learning","authors":"Xiaole Li ,&nbsp;Yinghui Jiang ,&nbsp;Xing Wang ,&nbsp;Jiuru Wang ,&nbsp;Lei Gao ,&nbsp;Shanwen Yi","doi":"10.1016/j.jnca.2025.104390","DOIUrl":"10.1016/j.jnca.2025.104390","url":null,"abstract":"<div><div>After some disaster occurs, rapid data evacuation among cloud data centers is of great importance. Data evacuation optimization is a two-stage process including destination selection and flow scheduling. These two stages are related to each other, while evacuation efficiency is affected by evacuation distance, bandwidth allocation ratio, and total amount of evacuation flow at the same time. The mutual constraints among multiple factors make it difficult to find or approximate the optimal solution via single-objective optimization. This paper proposes a new two-stage data evacuation strategy using multi-objective reinforcement learning, with evacuation flow optimization as the central optimization objective across both stages. In the first stage, it simultaneously minimizes total path length and maximizes the total available bandwidth to determine source–destination pair for every evacuation transfer. In the second stage, it simultaneously allocates proportional bandwidth and maximizes the total amount of evacuation flow to find path and allocate bandwidth for every evacuation transfer. Reward function is set based on classifying candidate sets to search for optimal solution while ensuring that feasible solutions are obtained. Chebyshev scalarization function is used to evaluate action rewards and optimize action selection process. Performance comparison is implemented with state-of-the-art algorithms based on different data volumes and network scales. Simulation result demonstrates that the new strategy outperforms other algorithms with higher evacuation efficiency, good convergence and robustness.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104390"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560057","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}

{"title":"An intelligent and explainable intrusion detection framework for Internet of Sensor Things using generalizable optimized active Machine Learning","authors":"Muhammad Hasnain ,&nbsp;Nadeem Javaid ,&nbsp;Abdul Khader Jilani Saudagar ,&nbsp;Neeraj Kumar","doi":"10.1016/j.jnca.2025.104358","DOIUrl":"10.1016/j.jnca.2025.104358","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Intrusion Detection (ID) in the Internet of Secure Things (IoST) has become increasingly critical due to the rising frequency and sophistication of cyber-attacks, which can lead to severe consequences such as data breaches, financial losses, and service disruptions. These risks are further intensified in computationally limited environments, where limited computational capacity and rapidly evolving threats make accurate and efficient detection challenging. In this study, a data-efficient ID framework tailored for resource-constrained environments is proposed by leveraging active learning and meta-heuristic optimization techniques. The proposed framework systematically addresses three critical limitations commonly observed in traditional models: data imbalance, inefficient hyperparameter tuning, and dependency on large labeled datasets. Initially, to mitigate class imbalance, adaptive synthetic sampling generates synthetic instances for minority classes, thereby enhancing learning in complex regions of the feature space. Next, for hyperparameter optimization, the Sandpiper Optimization (SO) algorithm fine-tunes the regularization parameter of Logistic Regression (LR), yielding significant improvements in model generalization. Finally, the challenge of limited labeled data is addressed through two active learning strategies: Active Learning Uncertainty-based (ALU) and Active Learning Entropy-based (ALE). These strategies selectively query the most informative samples from the unlabeled pool, ensuring maximum learning with minimal annotation effort. The performance of the proposed models is evaluated on two benchmark datasets: the wireless sensor networks and network intrusion detection datasets. Simulation results demonstrate that proposed models outperform base model LR. LRALE achieves improvements of 10.48% and 3.16% in accuracy, 19.48% and 3.16% in recall, and 7.23% and 1.04% in F1-score on WSN-DS and CIC-IDS-DS datasets, respectively. LRALU shows improvements of 18.18% and 2.11% in accuracy, 18.18% and 2.11% in recall, and 14.63% and 2.08% in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). Similarly, LRSO achieves improvements of 9.09% and 2.11% in accuracy, 9.09% and 1.05% in recall, and 9.76% and 3.12% in ROC-AUC on WSN-DS and CIC-IDS-DS datasets, respectively. To ensure model generalization and stability across different data partitions, a rigorous 10-fold cross-validation is conducted. Model interpretability is further enhanced using eXplainable artificial intelligence techniques, including Local interpretable model-agnostic explanations and Shapley additive explanations, to elucidate feature contributions and improve transparency. Additionally, statistical significance testing through paired &lt;em&gt;t&lt;/em&gt;-tests confirms the robustness and reliability of the proposed models. Overall, this framework introduces a comprehensive, annotation-efficient, and transparent ID solution that significantly advances the domain, m","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"245 ","pages":"Article 104358"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384600","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}