International Journal of Autonomous and Adaptive Communications Systems (6 papers in press)

Regular Issues

• Deep Learning based PSA Detection Model in Multi-user M-MIMO Networks  
  by Manju V.M, Ganesh R.S 
  Abstract: It is commonly known that MASSIVE MIMO (M-MIMO) is a key component for the forthcoming wireless networks. Base stations (BSs) in M-MIMO networks are fitted with an enormous number of antennas to provide several advantages over conventional MIMO, including easier power control, improved spectrum efficiency, and increased efficiency of energy. Since the estimated CSI might be contaminated by the eavesdrop interaction; M-MIMO systems are susceptible to pilot spoofing attacks (PSAs), which result in significant information leakage in the downstream transmission. To protectgenuine communications, this work introduces a new PSA detection model in multiuser M-MIMO (MU M-MIMO). Initially, signal transmission takes place and then the large scale fading factors are estimated. Further, PSA detection is done using Deep Neural Network (DNN) framework. Finally, the optimal channel estimation is done using Self Customized Black Widow Optimization Algorithm (SC-BWO). Moreover, analysis is performed on error probability, BER and so on.
  Keywords: M-MIMO; Pilot spoofing attacks; Multiuser; Deep Neural Network; SC-BWO Algorithm.
  DOI: 10.1504/IJAACS.2025.10067442
   
  
• MASCNN: Speech Emotion Recognition using Multi-head Area Self-Attention Convolutional Neural Network  
  by Qianli Ma, Wenjie Zhang, Yan Diqun 
  Abstract: Speech Emotion Recognition (SER) is pivotal for enhancing human-computer interaction by interpreting emotional expressions. Traditional machine learning approaches often encounter limitations in accuracy and adaptability. This study introduces a novel deep learning-based SER method incorporating attention mechanisms. Data augmentation techniques, including noise injection and speech modification, are applied prior to feature extraction. The Mel-frequency Cepstral Coefficient (MFCC) features are then processed using a Convolutional Neural Network (CNN) enhanced with a Multi-head Area Self-attention mechanism to improve emotion classification. Evaluation on the IEMOCAP database demonstrates that the proposed method outperforms existing SER techniques in recognition accuracy, with data augmentation significantly enhancing model performance. Additionally, guided class speech elicitation proves more effective than deductive class speech.
  Keywords: Speech Emotion Recognition; Attention Mechanism; CNN; IEMOCAP.
  DOI: 10.1504/IJAACS.2025.10068924
   
  
• Privacy-Preserving Outsourced Library Resource Data Sharing Scheme Based on Proxy Re-Encryption  
  by Tianzhang Li, Cai Mei, ChunXiao Zhang 
  Abstract: In the current digital era, libraries are increasingly incorporating cloud-based solutions to manage extensive collections and provide users with access to diverse resources. However, the adoption of cloud computing introduces critical challenges, particularly in ensuring data confidentiality and protecting user privacy. This paper proposes an innovative privacy-preserving library data sharing method based on proxy re-encryption, specifically designed for cloud environments. Our scheme enables secure outsourcing of library resources to the cloud, ensuring that only authorised users can access these resources. A key innovation of our approach is its ability to conceal users search queries from the cloud server, safeguarding query privacy. Moreover, library administrators can dynamically update encrypted data stored in the cloud without compromising security. Compared to existing methods, our work introduces a novel access control mechanism that enhances security while significantly reducingcommunicationcomplexity,requiringonlyoneroundofcommunication half the amount of prior approaches. Through comprehensive security analysis and performance evaluation, we highlight the distinct advantages of our method in maintaining data confidentiality, integrity, and operational efficiency
  Keywords: Privacy-preserving; Library resource; Proxy re-encryption; Outsourced.
  DOI: 10.1504/IJAACS.2025.10070012
   
  
• Software Security Assurance with an Augmented Software Component Analysis Approach for Open Source Component Evaluation  
  by Jian Hu, Linfei Li, Hailin Wang, Tao Chuan, Xiwei Dai, Yaodan Yu, Jie Wang 
  Abstract: Open source components are the foundation of modern software development, thereby making Software Component Analysis (SCA) as an essential method to ensuring software security. However, existing SCA methods concentrate on identifying open source component's security issues, ignoring comprehensive analysis with components' maintenance and support posture. In this paper, we investigated the literature from industry and academia about secure software development process, software and component evaluation models, and evaluation methodologies. Then, we proposed AOSCA, an augmented SCA approach by additionally quantifying a set of attributes for evaluation, despite of the conventional detected issues. The experimental results demonstrate that AOSCA can effectively assessing open source components and provide the evaluation result to software development organization based on requirements and preference. To sum up, AOSCA provides a comprehensive but effective mechanism for open source component evaluation. Applying AOSCA as a security practice during software development process is vital for software security assurance.
  Keywords: software security; software component analysis; open source component; security evaluation; augmented attribute.
  DOI: 10.1504/IJAACS.2025.10070268
   
  
• 5G-Powered Digital Twin for Orchestration and Prediction Task based on Cascaded LSTM  
  by Sridharan Kannan, Ahmad Y. A. Bani Ahmad, Thella Preethi Priyanka, Yogapriya J 
  Abstract: An advanced deep learning-based 5G-powered DT replica for orchestration and monitoring purposes is implemented in this paper. The major aim of this paper is the development of the MNDT replication, which is capable of being implemented in a 5G core environment. This model is found helpful in various advanced applications like Industry 4.0 and cyber security. The required model for the development of the MNDT is gathered by utilising a data acquisition approach. Further, the modelling is performed by making use of bidirectional connections within the digital and physical elements. Once the data collection is complete, and the MNDT is developed, then the second phase of the work is executed. Here, the prediction is carried out using the Cascaded Long Short-Term Memory (CasLSTM) framework. Experimental validations are carried out to prove the efficacy of the implemented deep learning-based 5G-powered MNDT for the prediction model.
  Keywords: 5G Technology; Digital Twin; Cascaded Long Short Term Memory; Data Acquisition; Orchestration and Prediction Task.
  DOI: 10.1504/IJAACS.2025.10071683
   
  
• A Robust Generative Coverless Co-steganography Method of Text and Image  
  by Yuxi Deng, Yun Tan, Le Mao, Xuyu Xiang 
  Abstract: Most existing coverless image steganography methods based on image generation suffer from insufficient hiding capacity. To address this, a Generative Coverless co-steganography method of text and image is proposed. Secret text is first converted into noise vectors using convolutional error-correcting codes, then a container image is generated using a self-attention generative adversarial network (SAGAN). The secret image is converted into a binary sequence through pixel encoding, and a style image containing the secret information is generated by an encoder. These images are then input into the generator to produce two sets of generative stego images for transmission. The secret text is recovered using a gradient descent method based on the simulated annealing algorithm, while the secret image is recovered through the decoder. Evaluated on CelebA and Fashion MNIST datasets, compared to existing methods, the proposed method significantly improves capacity and efficiency, increasing hiding capacity by 1.6 times.
  Keywords: Coverless steganography; Co-steganography of text and image; Generative adversarial networks.
  DOI: 10.1504/IJAACS.2025.10071727