Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

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.

Byzantine attack detection using optimized dual‐channel capsule generative adversarial network for securing cognitive radio network

“…𝜕℘ gen (16) where ℑ refers learning rate, Obj gen refers attack detection that is examined by 𝜉 smb randomly selection samples event, 𝜉 smb refers mini-batch size.…”

“…Also, it is the basic of collaborative spectrum sensing (CSS), in which, the secondary user sense the channel locally for primary user actions when guaranteeing the operation of primary user is not affected 13,14 . Various spectrum sensing approaches are available, among these; the common methods are matched filter, cyclo stationary detection 15 and energy detection model 16 . Then, the detection accuracy is improved by CSS signals that are affected through noise, multi‐path fading with shadowing, using spatial diversity that is placed in secondary users.…”

“…13,14 Various spectrum sensing approaches are available, among these; the common methods are matched filter, cyclo stationary detection 15 and energy detection model. 16 Then, the detection accuracy is improved by CSS signals that are affected through noise, multi-path fading with shadowing, using spatial diversity that is placed in secondary users. Fountain codes (FC) is used to combine the individual reports of entire participating secondary user.…”