The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

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An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two types of blockchain are constructed based on roadside units (RSUs) and Certificate Authorities (CAs), respectively. The proposed security scheme has multifold goals to identify malicious nodes and detect forged messages based on multiple factors, such as reputation of sender nodes, and time and distance effectiveness of messages. In addition, an incentive mechanism is introduced on the RSU blockchain to encourage RSUs to adopt active behaviors. Extensive simulations show that the proposed scheme exhibits superior performances to existing methods in detecting forged messages and identifying malicious nodes. Meanwhile, it provides privacy protection and improves the efficiency of vehicular networks.

/pdf/on-a-blockchain-based-security-scheme-for-defense-against-1q93cjm5.pdf

On a Blockchain-Based Security Scheme for Defense against Malicious Nodes in Vehicular Ad-Hoc Networks

Pulmonary disease is a kind of disease that affects the lungs and other parts of the respiratory system and is mainly caused by smoking, asbestos, secondhand smoke, and other forms of air pollutants. Several types of pulmonary diseases are emphysema, fibrosis, pneumothorax, asthma, lung cancer, chronic obstructive pulmonary disease (COPD), and so on. Pulmonary diseases are otherwise known as lung disorders and respiratory diseases. Pulmonary diseases are predicted by several methods using x-ray images and CT scan images. Some of the recent works predict only a specific disease, and optimal prediction is not yet achieved. Hence, we proposed a novel method known as African vulture optimization (AVO) algorithm-based weighted support vector machine approach (w-SVM). The proposed method in this article predicts emphysema, fibrosis, pneumothorax, and normal kinds from the NIH chest x-ray dataset. The X-images are preprocessed after data acquisition to obtain the desired size and to remove undesirable noise. The preprocessed images are then sent into the SVM for feature extraction, and the AVO is used to improve the SVM so that a kernel function may be obtained. The proposed w-SVM effectively predicts the emphysema, fibrosis, pneumothorax, and normal classes from the dataset. The experimental analyses are conducted and compared with existing works and concluded that the proposed work outperforms other approaches in terms of accuracy, sensitivity, specificity, and Matthews's correlation coefficient, prediction time, and modeling time.

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

A Secure Framework Based on Nature-Inspired Optimization for Vehicle Routing

Crowdsourcing is a procedure for demonstrating data outsourcing to a wide range of individual workers rather than considering a unique entity or a company. Crowdsourcing has made different kinds of chances for some trying issues by utilizing human knowledge. In order to attain an optimal global assignment technique, it is necessary to gather information regarding the location of the entire workers. There occur few security issues during information gathering that causes severe threat to all workers. To overcome the concerns based on privacy-preserving, this paper proposes a privacy-preserving model based on Fuzzy with the Black widow and Spider Monkey Optimization (BW–SMO). The fuzzy can be used to cluster the query solution. To optimize the query selection, we exploited the Black widow optimization algorithm incorporated with the Spider Monkey optimization algorithm. The parameters of both algorithms are controlled by the Fuzzy logic controller. Thus, our proposed frameworks of Fuzzy with BW–SMO effectively solve optimizing selection and join queries with low cost, latency, and securing data. The proposed model will be compared with the existing models to show the system's effectiveness.

Fuzzy with black widow and spider monkey optimization for privacy-preserving-based crowdsourcing system

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

S. Prabakeran

Papers

Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs