International Journal of Security and Networks (7 papers in press)

Regular Issues

• Tomato Disease Classification and Recognition using Machine Learning  
  by Bingjie Liu, Vladimir Y. Mariano 
  Abstract: Tomatoes are one of the primary vegetable foods in human society, yet they are susceptible to various diseases. This study used neural network algorithms to design an image recognition system for tomato leaf diseases. Based on the PlantVillage tomato dataset, the image data were pre-processed and normalised, and training and testing sets were selected and sent to three models: Convolutional Neural Network (CNN), Residual Network (ResNet), and Visual Geometry Group (VGG), for comparative training analysis. The experiment employed the cross-entropy function to balance sample differences, and the model training process was enhanced through reasonable parameter settings. Furthermore, the deployment challenges and solutions of the experimental model in real-world scenarios were discussed. After training, the VGG model performed the best, achieving a recognition accuracy rate of 84% and a precision rate of 83.5%.
  Keywords: Tomato disease classification; CNN; VGG; cross entropy.
  DOI: 10.1504/IJSN.2024.10069604
   
  
• Safety Assessments of 5G Antenna with Angle Steel Reinforced Transmission Tower based on Full-Scale Test and BP Neural Network  
  by Weizhou Xu, Yashan Hu, Zengju An 
  Abstract: Installing 5G antennas on electrical transmission towers could save cost and space, especially in urban areas. Nevertheless, the safety of the 5G shared transmission tower is rarely reported but is a concerned by engineers. This study performed full-scale experiments on the effects of 5G antennas on the transmission towers, subject to self-weight and wind loads. It revealed that the 5G antennas would not affect the mechanical behaviour of joint areas but the main steel members of transmission towers. Consequently, 5G antennas would probably impair the safety of transmission towers. The effects of 5G antennas on the natural frequency of the transmission towers are investigated by the soil-pile-tower-line-5G finite element model. To facilitate the promotion and quick safety assessment of 5G shared transmission towers, a comprehensive database is constructed based on the finite element model, considering the voltage levels, tangent/tension tower, span lengths, and quantity of 5G antennas. A backpropagation (BP) neural network trained on this database achieves rapid safety evaluations with < 1% relative error for 110 kV towers, enabling engineers to assess 5G-shared towers without complex simulations.
  Keywords: transmission tower; 5G; safety assessment; database; back propagation neural network.
  DOI: 10.1504/IJSN.2025.10071726
   
  
• Intelligent Fault Diagnosis of New Energy Vehicle Charging Piles Using Data-Driven Deep Learning in Smart Charging Networks  
  by Qingchao Zeng 
  Abstract: New energy vehicle charging infrastructure demands reliable fault diagnosis to ensure stable smart grid operations. Hardware failures, communication errors, and power anomalies disrupt systems, risking safety and efficiency. Existing methods rule-based models and classical machine learning struggle with real-time processing, complex fault patterns, and dynamic environments, leading to delayed or inaccurate detection. This study introduces the smart charging fault diagnosis framework (SCFDF), a data-driven deep learning approach that processes real-time charging data to extract features and classify faults via neural networks. Unlike static models requiring manual thresholds, SCFDF continuously learns from historical and live data, enhancing early anomaly detection and enabling predictive maintenance. It supports real-time monitoring, optimises energy use, reduces downtime, and strengthens operational reliability in smart charging networks. Applications include smart grids, EV charging systems, and intelligent energy management. Future integration with reinforcement learning and edge computing aims to further boost efficiency. Experimental results demonstrate an 18% accuracy improvement over conventional methods, highlighting SCFDFs potential to advance robust, adaptive charging infrastructure. This framework offers scalable solutions for evolving energy ecosystems, prioritising proactive maintenance and seamless energy distribution.
  Keywords: Fault Diagnosis; New Energy Vehicle; Charging Pile; Deep Learning; Smart Charging Networks; Data-Driven Approach; Predictive Maintenance; Intelligent Energy Management.
  DOI: 10.1504/IJSN.2025.10071734
   
  
• An Automatic Access Control based on an Improved CP-ABE algorithm in Wireless Sensor Networks  
  by Zhihao Yin, Haowen Zha, Canyu Yang, Qidong Ling 
  Abstract: Existing security management methods in wireless sensor networks typically employ asymmetric key encryption algorithms for automatic access control, resulting in high time overhead. In wireless sensor networks, different nodes may have different roles. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can dynamically adapt to diverse privilege requirements without assigning separate keys to each user. Therefore, an enhanced access control method is proposed based on an improved CP-ABE algorithm. First, we proposed a distributed CP-ABE algorithm supporting efficient revocable and outsourced computation. Second, based on the historical interaction records, the dynamic user identity trust degree is calculated and added to the access structure. The security analysis and simulation results indicate that the proposed method not only satisfies the indistinguishability under selective plaintext attack but also meets the real-time requirement, with the average time overhead of access control reaching only 42.5 ms when the number of concurrent access users is 100.
  Keywords: wireless sensor network; access control; CP-ABE; attribute revocation; outsourcing calculation; trustworthiness calculation.
  DOI: 10.1504/IJSN.2025.10071997
   
  
• Optimised Deep Learning-Based Intrusion Detection for Ethereum Blockchain Framework for Secure Data Sharing  
  by Anju Raveendran, R. Dhanapal 
  Abstract: This paper proposes a secure data-sharing model that utilises a blockchain-based secure framework and a deep learning-based intrusion detection model to ensure patient privacy and provide personalised healthcare services. The proposed model consists of two phases: validation and verification. In the validation phase, electronic health record (EHR) data is uploaded to an Ethereum blockchain, encrypted using improved elliptic curve cryptography (Imp-ECC), and stored in an InterPlanetary File System (IPFS) within the blockchain. In the verification phase, an optimised deep-learning approach, Enhanced Capsule-BiLSTM, is used to detect unauthorised users in the network. If an attack is detected, access is denied; otherwise, the user is authorised to access the encrypted data. The proposed model is evaluated using two datasets, EHR and UNSW-NB15. The results show that the proposed model achieves a less encryption time of 198 seconds for the EHR dataset and an accuracy of 97.19% for the UNSW-NB15 dataset.
  Keywords: Blockchain-based secure framework; IPFS system; Encryption; Attack detection; EHR data security; Improved Bald Eagle optimisation; Ethereum blockchain; Improved elliptic curve cryptography.
  DOI: 10.1504/IJSN.2025.10072002
   
  
• An Efficient Cooperative Localisation Method Using Sub-network Division and Hybrid Optimisation Algorithm  
  by Luoping Liao, Xiaoyong Yan, Rong Li 
  Abstract: For applications in the internet of things, location information of nodes is important. Conventional localisation methods have three main problems: 1) high communication costs; 2) lower distance measurement accuracy; 3) unstable localisation in uneven node deployments. To this end, this paper proposes an efficient cooperative localisation method using sub-network division and hybrid optimisation. During network initialisation, this method concurrently measures node pair distances using the Salton similarity metric. Using the shared error distribution, we calculate the maximum hop threshold to remove outliers. Next, within the hop count threshold, we divide the entire network into optimal sub-networks. Finally, the hybrid jellyfish and crow search algorithm are introduced to estimate the locations of unknown nodes in sub-networks. Simulation results show that the method significantly outperforms existing approaches in efficiency, accuracy, and stability across various irregular networks. Specifically, the method improves the localisation accuracy by 29.37%93.04% over the current methods.
  Keywords: Cooperative localization; Irregular networks; Similarity metric; Sub-network division; Hybrid optimization algorithm.
  DOI: 10.1504/IJSN.2025.10072004
   
  
• A Network Security Situation Assessment based on Atomic Search Optimised Fuzzy Neural Network  
  by Feiyue Yu 
  Abstract: Modern cyberspace faces threats from sophisticated attacks such as distributed denial-of-service, advanced persistent threats, and zero-day exploits, demanding real-time and adaptive security assessment frameworks. Traditional methods struggle with dynamic threat landscapes due to their limited adaptability to high-dimensional data, susceptibility to fuzzy rule conflicts, and reliance on gradient-based optimization prone to local optima. This study proposes an atomic search-optimised fuzzy neural network model to address the challenges of real-time adaptability, high-dimensional data processing, and fuzzy rule conflicts in network security situation assessment. Integrating quantum-enhanced atom search optimisation with a five-layer fuzzy neural architecture enables the proposed model to dynamically co-optimize fuzzy membership functions and neural network parameters through Lennard-Jones potential-driven population evolution. Key innovations include a fuzzy entropy-based adaptive feature weighting mechanism and a Spark-enhanced atom search optimisation with a five-layer fuzzy neural architecture.
  Keywords: network security situation assessment; Atom Search Optimization; Fuzzy Neural Network.
  DOI: 10.1504/IJSN.2025.10072026