International Journal of Internet Protocol Technology (4 papers in press)

Regular Issues

• Faultload Sequences for the MQTT Protocol Services  
  by Amina Jandoubi, M.Taha Bennani, Olfa Mosbahi 
  Abstract: We have proposed preliminary results of a new approach to extract the sequences of events that model the fault injection times at the MQTT messaging protocol level to assess their reliability. After targeting publish service, we got seven possible scenarios to put the system in a faulty state. We introduced new algorithms to: (1) extract send and receive events from CFGs, (2) identify send/receive pairs, (3) time stamp the events using a vector clock, (4) filter sending events, and (5) generate alternative send sequences. In this paper, we extended the initial results by applying the approach to the services provided by the Publisher, the Subscriber, and the MQTT Broker, which increased the number of scenarios up to seventeen fault injection sequences. This contribution is the first step in formalising the description of all possible attacks on the MQTT protocol, which is crucial to reinforce the reliability of its services.
  Keywords: MQTT; Internet of things; Fault injection; Control-Flow Graphs.
  DOI: 10.1504/IJIPT.2026.10072721
   
  
• Risk Assessment of Network Public Opinion Information Circle based on CAFA-BP Algorithm  
  by Qianqian Zheng 
  Abstract: To address the issues of low coverage of influencing factors, low evaluation accuracy, and long response time in current risk assessment methods for network public opinion information circle dissemination, a risk assessment method of network public opinion information circle based on CAFA-BP algorithm is proposed. Optimise the LSTM model through attention mechanism, use the optimized LSTM model to predict the popularity of network public opinion, and determine the factors affecting communication risk based on the popularity of network public opinion. Using CAFA algorithm to optimise BP neural network, establish a risk assessment model based on CAFA-BP algorithm, and use this model to determine the risk value of network public opinion information circle propagation. The experimental results show that the maximum coverage rate of the influencing factors of the proposed method is 98.56%,the evaluation accuracy varies between 94.58% and 97.62%, and the response time varies between 1.23s and 1.69s.
  Keywords: CAFA-BP algorithm; Network public opinion information circle; Risk assessment; Popularity of network public opinion.
  DOI: 10.1504/IJIPT.2025.10072909
   
  
• Corporate Financial Risk Prediction Model Based on Deep Learning  
  by Fei Xue 
  Abstract: The correlation between financial success and corporate social responsibility (CSR) has been the subject of extensive research in recent years While prior studies have explored this relationship, objectively assessing the effectiveness of CSR programs has remained a considerable challenge However, advancements in research methodologies and the development of Environmental, Social, and Governance (ESG) measurement criteria have facilitated more reliable evaluations With the rapid advancement of artificial intelligence, and deep learning (DL) techniques have been increasingly applied across various domains The application of machine learning to ESG data analysis remains relatively limited Consequently, deep learning methodologies have proven to be practical, efficient, and effective approaches for predicting corporate financial performance based on ESG metrics Through risk management optimization, accuracy and recall rates have improved to 0 905 and 0 890, respectively The proposed risk prediction model achieves high accuracy rates of 90 5% and 92 4% in the training and test data sets, respectively.
  Keywords: Key performance indicator (KPI); Sustainable Development Goal (SDG); Return on equity (ROE); spiking neural networks (SNNs); artificial neural network (ANN); Deep learning (DL); machine learning (ML).
  DOI: 10.1504/IJIPT.2025.10072912
   
  
• Effective Information Classification Mining Method for IoT Big Data Based on Support Vector Machine  
  by Shumin Zhi 
  Abstract: The effective information classification mining of IoT big data is of great significance in enhancing data value, optimising resource allocation, and promoting technological innovation to solve the problems of low recall and precision, and long completion time in current methods. Research on effective information classification mining method for IoT big data based on support vector machine has been carried out. By optimising the selection of initial k values and initial clustering centre points, the improved K-means clustering algorithm is utilized to achieve big data collection in the Internet of Things. By predicting residuals for anomaly detection and removal of IoT data, the removed IoT big data is input into a fuzzy support vector machine to achieve information classification mining. The experimental results show that the average recall rate of the proposed method is 97.07%,the average precision rate is 96.72%, and the task completion time varies between 0.22s and 0.68s.
  Keywords: Support vector machine; IoT; Big data; Effective information; Classification mining; Improved K-means clustering algorithm.
  DOI: 10.1504/IJIPT.2025.10072914