Computational Efficiency and Scalability

The gap

Consensus across the literature

Supporting evidence — 2 representative gaps

• Enhancing security in iov: an ensemble learning approach for DDoS detection (2026) · doi

  This paper addressed the key limitations of existing IDSs in the IoV. These limitations include poor detection of diverse DDoS attack types and weak adaptability to high network density, dynamic topology, and limited computational resources. Such issues reduce IDS effectiveness in real-time vehicular environments. To overcome these challenges, an ensemble learning– based IDS was proposed. The framework integrates two optimized CNN models for both binary and multi-class classification. The system effectively detects twelve types of DDoS attacks and adapts to changing IoV conditions. A dynamic deployment algorithm was also introduced to select the best IDS location among fog nodes, RSUs, and UAVs. This ensures continuous detection during congestion and partial system failures. In addition, a game-theoretic optimization strategy was designed to activate the IDS only when an attack is likely. This approach reduces energy con- sumption and limits unnecessary processing overhead. The proposed system was evaluated using three bench- mark datasets: VDoS-LRS, CICDDoS2019, and VDDD. The experimental results showed detection accuracy of over 99%. The proposed method outperformed existing approaches in terms of accuracy, scalability, adaptability, and efficiency. The simulation results also confirmed the effectiveness of the game-theoretic mechanism in limiting attacker advantage. For future work, advanced feature selection techniques such as information gain and Fast Correlation-Based Filter (FCBF) will be investigated. More efficient image-based packet representation methods will also be explored. These improvements aim to reduce per-packet classification time and enhance real-time performance in large-scale IoV systems. Authors’ contributions S.H. and Z.J. designed the study. Z.J. and S.S. and A.M. implemented the proposed model and conducted the experi- ments. S.H. and Z.J. analyzed the results and prepared figures. Z.J. and S.S. and A.M. wrote the main manuscript text. All authors reviewed and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received for conducting this study. Data availability No datasets were generated or analysed during the current study.

  Keywords: proposed detection time based system authors limitations existing ddos attack types adaptability dynamic reduce effectiveness
• Malware Detection Using Machine Learning Techniques (2026) · doi

  The scalability of the X-Adapt Boost model to large-scale malware datasets and its computational efficiency compared to existing methods require further investigation.

  Keywords: scalability adapt boost model large scale malware datasets computational efficiency compared existing require further investigation