International Journal of Vehicle Autonomous Systems (4 papers in press)

Regular Issues

• Machine intelligence-based security system to protect smart vehicles from relay attacks  
  by Anurag R. Simha, Bharat Kumar Naik, C. Rekha, Vinod L. Navhi, K. Vinodha 
  Abstract: With the advancement of technology and the expansion of IoT have reduced the gap between humans and machines in recent years. IoT systems are always vulnerable to attacks from devilish minds on Earth, and these might be sufficient to bring down a whole company in the marketplace. There are incidents of smart car thefts that occur covertly due to a well-known exploit called the Relay attack. Its crucial to properly neutralise the relay attack since there are situations where multiple users are operating an automobile at once. By leveraging the current innovation, we aim to mitigate the method that cybercriminals use to easily break into vehicles. The most serious issue is the Relay Attack, which could use a latent keyless entry system weakness to start a Passive Keyless Entry System vehicle. An adversarial attacker gains entry to a mechanised vehicle by intercepting radio messages and relaying them back to the vehicle. The effect of this assault and the misfortune is huge. In this work, an amalgamation of network security, the Internet of Things and machine intelligence is considered to formulate a countermeasure for relay attacks. The proposed system ensures the security of vehicles that work on a passive keyless entry system by authenticating multiple drivers through the CART algorithm.
  Keywords: relay attack; PKES; IoT; network; machine learning; driver identification; cyber security.
  DOI: 10.1504/IJVAS.2025.10075927
   
  
• Enhancing smoothness in a sunflower harvester machine using cabins RMI and seats NSS  
  by Jiaxi Lu, Yang Liu, Yanyan Li, Zhihong Zhou 
  Abstract: This study proposes a Negative-Stiffness Structure (NSS) and Rubber Material Insulation (RMI) to support the seat suspension and cabins floor of the Sunflower Harvester Machine (SHM) for enhancing the drivers riding smoothness. Its efficiency was analysed via the root mean square acceleration of the drivers seat (RMSas) under various conditions. The study showed that the drivers riding smoothness was very poor with the Original Structures (OS) of SHM. By using the seats NSS and the cabins RMI with their optimal parameters, the value of RMSas was decreased by 63.6% and 79.8% compared to OS. Particularly, by using both the seats NSS and the cabins RMI (NSS-RMI), the value of RMSas was strongly reduced by 95.2% in comparison with OS. This result was stable under different vehicle operating conditions. This study implied that NSS-RMI should be designed on SHM to enhance its smoothness.
  Keywords: vibration models; sunflower harvester machine; riding smoothness; negative stiffness structure; cabin's rubber material insulation.
  DOI: 10.1504/IJVAS.2025.10076156
   
  
• Application of mobile network traffic analysis in designing audio processing systems for Western orchestra composition  
  by Jing Liang Wang 
  Abstract: Mobile network technology and audio processing systems enable high-quality applications, requiring efficient traffic transmission and management. Thus, mobile network traffic classification with advanced techniques in particular, hybrid deep learning models will help improve network performance with real-time audio streaming applications. CNNs and RNNs improve traffic classification, enhancing QoS across issues. The results obtained from the experiments were very good since the accuracy, precision, recall and F1-score were 98.8%, 99.2%, 98.3% and 98.8%, respectively. The model enhances remote orchestral collaboration with smooth high-quality audio transmission. The great thing about this project is that it merges deep audio feature learning with mobile network traffic optimisation for orchestral streaming through a new hybrid CNN-RNN model. This is a cross-domain approach that intelligently links and relates the audio spectrogram features to network flow characteristics around a low latency, adaptable orchestral streaming service.
  Keywords: mobile network traffic classification; audio processing; Western orchestra; hybrid CNN-RNN model; spectrogram.
  DOI: 10.1504/IJVAS.2025.10076422
   
  
• DDoS attack detection in vehicular ad-hoc network: a survey  
  by Prakash Sontakke, Taniya Bhattacharjee 
  Abstract: The primary goal of an intrusion detection system (IDS) is to detect potential threats within a network by thoroughly examining extensive data volumes. With the growing interconnectivity of devices, the role of intrusion detection in network security is increasingly crucial. Diverse machine learning techniques and methodologies have been integrated to enhance the accuracy of IDS, aiming to minimize false alarms and augment detection rates. The core function of IDS lies in its ability to monitor and scrutinize network traffic for any suspicious activities. A well-structured classification system is imperative to address this challenge, and prevalent machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, XG Boost, and Voting Ensemble are commonly applied. These strategies play a pivotal role in surmounting categorization obstacles and optimizing the performance of intrusion detection systems when dealing with extensive datasets.
  Keywords: distributed denial of service; vehicular ad hoc network; software-defined network; threshold violation counter DoS attack; time to attack; CPU utilisation; memory utilisation.
  DOI: 10.1504/IJVAS.2026.10076617