TY  - JOUR
AU  - Khan, Fida Muhammad
AU  - Rahman, Taj
AU  - Zeb, Asim
AU  - Haider, Zeeshan Ali
AU  - Khan, Inam Ullah
AU  - Bilal, Hazrat
AU  - Khan, Muhammad Abbas
AU  - Ullah, Inam
PY  - 2024
DA  - 2024/12/31
TI  - Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 1
IS  - 2
SP  - 136
EP  - 153
DO  - 10.62762/TSCC.2024.626147
UR  - https://www.icck.org/article/abs/TSCC.2024.626147
KW  - vehicular networks security
KW  - denial of service (DoS) detection
KW  - deep learning intrusion detection
KW  - feature optimization techniques
KW  - connected vehicle protection
AB  - In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projection (RP) and Principal Component Analysis (PCA). This framework extracts and analyzes significant features using a publicly available application-layer DoS attack dataset, achieving higher detection accuracy than traditional methods. Experimental results indicate that combining CNN, LSTM networks, and DBN with feature selection techniques like Random Projection (RP) and PCA results in improved classification performance, achieving an accuracy of 0.994, surpassing the state-of-the-art machine learning models. This novel approach enhances the reliability and safety of vehicle communications by providing efficient, real-time threat detection. The findings contribute significantly to VANET security, laying a robust foundation for future advancements in connected vehicle protection.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Khan2024Vehicular,
  author = {Fida Muhammad Khan and Taj Rahman and Asim Zeb and Zeeshan Ali Haider and Inam Ullah Khan and Hazrat Bilal and Muhammad Abbas Khan and Inam Ullah},
  title = {Vehicular Network Security Through Optimized Deep Learning Model with Feature Selection Techniques},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2024},
  volume = {1},
  number = {2},
  pages = {136-153},
  doi = {10.62762/TSCC.2024.626147},
  url = {https://www.icck.org/article/abs/TSCC.2024.626147},
  abstract = {In recent years, vehicular ad hoc networks (VANETs) have faced growing security concerns, particularly from Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks flood the network with malicious traffic, disrupting services and compromising resource availability. While various techniques have been proposed to address these threats, this study presents an optimized framework leveraging advanced deep-learning models for improved detection accuracy. The proposed Intrusion Detection System (IDS) employs Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Belief Networks (DBN) alongside robust feature selection techniques, Random Projection (RP) and Principal Component Analysis (PCA). This framework extracts and analyzes significant features using a publicly available application-layer DoS attack dataset, achieving higher detection accuracy than traditional methods. Experimental results indicate that combining CNN, LSTM networks, and DBN with feature selection techniques like Random Projection (RP) and PCA results in improved classification performance, achieving an accuracy of 0.994, surpassing the state-of-the-art machine learning models. This novel approach enhances the reliability and safety of vehicle communications by providing efficient, real-time threat detection. The findings contribute significantly to VANET security, laying a robust foundation for future advancements in connected vehicle protection.},
  keywords = {vehicular networks security, denial of service (DoS) detection, deep learning intrusion detection, feature optimization techniques, connected vehicle protection},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}