###### Abstract  
The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy. m 2022 14  
with IoV to make it CR-IoV [7, 8]. The integrated CR-IoV achieves better data rates and transmission efficiency. In recent times, Millimeter Wave (mmWave) communication is expected to meet the huge capacity requirements of future Internet of Things (IoT), 5G, and IoV applications [9, 10]. It is also capable of functioning with CRN networks for attaining better data transmission efficiencies [11]. In CR-IoV, security is the major concern because of the involvement of various attackers [12]. In particular, CR-based attacks are more harmful since most of the attacks affect spectrum sensing and accessing. With the improper spectrum sensing results, the spectrum utilization will be degraded. The most common attacks in CR-IoV are Spectrum Sensing Data Falsification (SSDF) attacks and Primary User Emulation (PUE) attacks [13, 14]. In the SSDF attack, the attacker attempts to send false sensing reports on the PU absence to the fusion center (FC) or cognitive base station (CBS) [15]. With the false sensing reports, CBS tends to make a wrong decision on the presence of PU. In sum, the SSDF attack severely affects spectrum sensing reliability, further affecting spectrum utilization. In the PUE attack, the attacker mimics the PU behavior to obscure the legitimate SUs [16]. In the presence of a PUE attacker, the SUs vacate the available spectrum since the attacker behaves like PU. Due to the PUE attack, the SUs are vetoed from accessing the available spectrum. For SSDF detection and mitigation, Machine Learning (ML) algorithms have been utilized widely. For instance, Multi-Layer Perceptron (MLP) neural network detects the SSDF attackers based on the behavior and isolates the attackers from the network [17]. An effective response to dynamic security attacks can be achieved through the use of symmetry-adapted machine learning [18].