A hybrid deep learning based enhanced and reliable approach for VANET intrusion detection system

被引:5
作者
Barve, Atul [1 ]
Patheja, Pushpinder Singh [1 ]
机构
[1] VIT Bhopal Univ, SCSE, Bhopal, MP, India
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2024年 / 27卷 / 09期
关键词
VANET; IDS; CNN; BiLSTM; MISBEHAVIOR DETECTION; NETWORK;
D O I
10.1007/s10586-024-04634-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Advances in autonomous transportation technologies have profoundly influenced the evolution of daily commuting and travel. These innovations rely heavily on seamless connectivity, facilitated by applications within intelligent transportation systems that make effective use of vehicular Ad- hoc Network (VANET) technology. However, the susceptibility of VANETs to malicious activities necessitates the implementation of robust security measures, notably intrusion detection systems (IDS). The article proposed a model for an IDS capable of collaboratively collecting network data from both vehicular nodes and Roadside Units (RSUs). The proposed IDS makes use of the VANET distributed denial of service dataset. Additionally, the proposed IDS uses a K-means clustering method to find clear groups in the simulated VANET architecture. To mitigate the risk of model overfitting, we meticulously curated test data, ensuring its divergence from the training set. Consequently, a hybrid deep learning approach is proposed by integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. which results in the highest training, testing, and validation accuracy of 99.56, 99.49, and 99.65% respectively. The results of the proposed methodology is compared with the existing state-of-the-art in the same domain, the accuracy of the proposed method is raised by maximum of 4.65% and minimum by 0.20%.
引用
收藏
页码:11839 / 11850
页数:12
相关论文
共 25 条
[11]  
2005, VEHICLE SAFETY COMMU
[12]  
Dutta N., 2013, P INT C ADV VEH SYST, P21
[13]   QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks [J].
Fatemidokht, Hamideh ;
Rafsanjani, Marjan Kuchaki .
JOURNAL OF SYSTEMS AND SOFTWARE, 2020, 165
[14]   Hybrid and Multifaceted Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network [J].
Ghaleb, Fuad A. ;
Maarof, Mohd Aizaini ;
Zainal, Anazida ;
Al-Rimy, Bander Ali Saleh ;
Saeed, Faisal ;
Al-Hadhrami, Tawfik .
IEEE ACCESS, 2019, 7 :159119-159140
[15]   Misbehavior detection and efficient revocation within VANET [J].
Hasrouny, Hamssa ;
Samhat, Abed Ellatif ;
Bassil, Carole ;
Laouiti, Anis .
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2019, 46 :193-209
[16]  
Kandali K, 2023, International Journal of Electrical and Computer Engineering (IJECE), V13, P3140, DOI [10.11591/ijece.v13i3.pp3140-3148, DOI 10.11591/IJECE.V13I3.PP3140-3148]
[17]   Hybrid optimization enabled trust-based secure routing with deep learning-based attack detection in VANET [J].
Kaur, Gurjot ;
Kakkar, Deepti .
AD HOC NETWORKS, 2022, 136
[18]  
Krajzewicz D., 2012, International Journal On Advances in Systems and Measurements, V5
[19]   Analyzing Attack Strategies Against Rule-Based Intrusion Detection Systems [J].
Parameshwarappa, Pooja ;
Chen, Zhiyuan ;
Gangopadhyay, Aryya .
PROCEEDINGS OF THE WORKSHOP PROGRAM OF THE 19TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING (ICDCN'18), 2018,
[20]   Rule-Based Network Intrusion Detection System for Port Scanning with Efficient Port Scan Detection Rules Using Snort [J].
Patel, Satyendra Kumar ;
Sonker, Abhilash .
INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING, 2016, 9 (06) :339-350