Abstract
Intelligent transportation systems have been envisioned to bring more intelligence and cooperative sensing to meet the imminent demands of overall improved autonomous transportation. However, dynamic era of modern applications and fixed architecture of legacy Internet needs flexible, innovative, adaptive, and programmable software defined intelligent transportation systems (SD-ITS). The centralized control intelligence of SD-ITS can be a potential primary target of the prevalent cyber threats and attacks that can simply throw the entire network into chaos. The authors propose a DL-driven multi-vector scalable attack detection framework leveraging graphical processing unit (GPU) empowered Bidirectional Long Short-Term Memory (BLSTM) to efficiently tackle exponentially growing diverse sophisticated attacks that primarily target the control unit of the SD-ITS. The proposed technique has been rigorously evaluated with current state-of-the-art publicly available Flow-based dataset (i.e., CICIDS2017) using standard performance metrics. Further, the proposed mechanism is compared with contemporary benchmarks (i.e., DL algorithms). Extensive experimental results exhibit out-performance of the proposed technique in term of detection accuracy with a trivial trade-off computational complexity. Finally, the study also employed 10-fold cross validation to explicitly show unbiased results.