A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETs

In the information times, with the rapid development of network technology, emerging technologies such as big data and artificial intelligence have gradually entered daily life, but various network security risks have also followed. In recent years, as a new security strategy, mimic defense technology has been a new force suddenly rises, providing a new idea and direction for protecting network security. In the actual application scenario, the resource consumption of the mimic defense service is large, and it is generally deployed and provided by the cloud service provider. Although the mimic defense can play a good role in defending against external attacks, the data transmission in the current system is based on plaintext, which cannot guarantee the privacy of user data. Homomorphic encryption technology, as an encryption supporting data ciphertext operation, can protect data privacy on the premise of completing data operation requirements. As an important field of homomorphic technology, homomorphic hash technology has been widely used in cloud storage scenarios. Aiming at the problem that the cloud service mimic defense system cannot protect data privacy, this paper introduces homomorphic encryption technology into the mimic defense system to protect the privacy of user data. The scheme proposed in this paper is based on the mimic defense architecture, which optimizes the adjudication process of the adjudication module through the homomorphic encryption technology, make full use of the characteristics of homomorphic operation, protects the privacy of user data, and improves the system security. Moreover, this paper builds a mimic routing platform for experimental analysis. The experimental results show that the malicious attacker can see the user’s private information under the traditional defense mechanism. However, after homomorphic encryption optimization, each user authentication is still less than 1 millisecond, and the user information data become invisible to malicious attackers.

Homomorphic encryption technology can analyze the data stored in the cloud without decryption, because the results of ciphertext calculation after decryption are the same as the corresponding plaintext calculation results. Based on homomorphic encryption and machine learning technology, this paper proposes a K-nearest neighbor classifier based on homomorphic encryption scheme, Homomorphic encryption technology can not only ensure the security of the data, but also analyze the data in the ciphertext state since the characteristics of homomorphism, avoiding the data insecurity problem caused by analyzing the data after decryption in the clound. In this scheme, we first improve the ciphertext comparison algorithm and improve the judgment of sample label in ciphertext state. Then, using k-nearest neighbor classifier, a ring based selection algorithm is designed to reduce the time of ciphertext operation. The results show that our scheme can realizes the ciphertext classification On the condition of ensuring the accuracy of classification. Compared with the original k-nearest neighbor classification method, the classification accuracy of the our algorithm is improved about 1%, but the time cost is larger than the original k-nearest neighbor classification method.

The vehicular ad hoc network (VANET) is a platform for exchanging information between vehicles and everything to enhance driver’s driving experience and improve traffic conditions. The reputation system plays an essential role in judging whether to communicate with the target vehicle based on other vehicles’ feedback. However, existing reputation systems ignore the privacy protection of feedback providers. Additionally, traditional VANET based on wireless sensor networks (WSNs) has limited power, storage, and processing capabilities, which cannot meet the real-world demands in a practical VANET deployment. Thus, we attempt to integrate cloud computing with VANET and proposes a privacy-preserving protocol of vehicle feedback (PPVF) for cloud-assisted VANET. In cloud-assisted VANET, we integrate homomorphic encryption and data aggregation technology to design the scheme PPVF, in which with the assistance of the roadside units (RSU), cloud service provider (CSP) obtains the total number of vehicles with the corresponding parameters in the feedback for reputation calculation without violating individual feedback privacy. Simulation results and security analysis confirm that PPVF achieves effective privacy protection for vehicle feedback with acceptable computational and communication burden. Besides, the RSU is capable of handling 1999 messages for every <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300ms$ </tex-math></inline-formula> , so as the number of vehicles in the communication domain increases, the PPVF has a lower message loss rate.

In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.

From the past decade, vehicular ad hoc network (VANET) is turning up as a promising field for academic researchers and the automotive industry. Nowadays, intelligent transportation system (ITS) is one of the most emerging application areas of VANETs. According to a survey by the World Health Organization (WHO), nearly 1.35 million individuals die every single year in road accidents. VANET can be a good prospective for the improvement in traffic safety, efficiency of roadside traffic and effortless performance. Moreover, various services such as infotainment services and billing services for toll can be provided to both driver and passenger of the vehicle. VANET, the special category of mobile ad hoc networks (MANETs), is a network in which nearby vehicles can converse with each other or can converse with nearby stationary equipment placed on the road to make use of dedicated short-range communication (DSRC) protocol. VANETs are themselves susceptible to attacks, which causes the security breach in it and results in loss of time, money, efforts and even lives. The implementation of a comprehensive, secure and robust VANET system is a challenging and time-consuming activity and requires a prodigious effort. This chapter systematically reviews research on VANET. First, VANET and its application areas are discussed. Subsequently, the layered architecture of VANET and challenges in VANET are explored. Afterward, the key issues in VANET, i.e., security and privacy, are explored. The security is a vital parameter of VANET and deals with various issues associated with threats and attacks. In VANET, authentication is the most crucial security service. Later, authentication is classified based on cryptography, signature and verification. Finally, various research areas in VANETs are explored. The main objective of this chapter is to discuss various challenges related to VANETs, especially security and authentication to overcome from the misuse of private data along with the necessary suggestions to improve the existing architecture.

Inspecting VANET with Various Critical Aspects – A Systematic Review

Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review

Dynamic nature of Vehicular Ad-hoc Networks (VANETs) and Wireless Sensor Networks (WSN) makes them hard to deal accordingly. For such dynamicity, Machine learning (ML) approaches are considered favourable. ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network. In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs. Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed. We have listed some of the ML techniques to take additional consideration of this emergent field. A summary is given for ML techniques application with their complexities to cover on open issues to kick start further research investigation. This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.

Vehicular network plays a major role in understanding the detail study of vehicle communications. Multiple vehicles in local communication range need to exchange the safety and infotainment information via common roadside infrastructure in Vehicular Ad hoc Networks (VANETs). Vehicle-to-Infrastructure (V2I) communication model help to improve the efficiency of intelligent transport system by providing safety warnings and reducing vehicle collisions. Machine learning is an artificial intelligence component that gives the machine an ability to automatically learn without being expressly trained to improve from experience. Since VANET is imprecise and uncertain in nature, Machine Learning (ML) and Software Agents (SAs) combining approaches resolve the issues of V2I communication challenges in VANETs. This paper proposes ML based V2I Communication in VANETs using software agent approach. The proposed agent-based model is made up of both static and mobile agents. Proposed model executes decision tree algorithm and Q-Learning algorithm to identify the event as non critical or critical and the destination vehicle respectively to improve bandwidth utilization, packet delivery ratio and end-to-end delay.

&lt;span lang="EN-US"&gt;Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.&lt;/span&gt;

Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An effective cybersecurity measure is essential as security threats impact the overall system, from business disruptions to data corruption, theft, exposure, and unauthorized network access. Intrusion detection systems (IDSs) are popular cybersecurity measures that detect intrusive behavior in a network. Recently, the machine learning (ML)-based IDS has emerged as a new research direction in VANET security. ML-based IDS studies have focused on improving accuracy as a typical classification task without focusing on malicious data. This study proposes a novel IDS for VANETs that offers more attention to classifying attack cases correctly with minimal features required by applying principal component analysis. The proposed Cascaded ML framework recognizes the difference between the attack and normal cases in the first step and classifies the attack data in the second step. The framework emphasizes that an attack should not be classified into the normal class. Finally, the proposed framework is implemented with an artificial neural network, the most popular ML model, and evaluated with the Car Hacking dataset. In addition, the study also investigates the efficiency of typical classification tasks and compares them with results of the proposed framework. Experimental results on the Car Hacking dataset have revealed the proposed method to be an effective IDS and that it outperformed the existing state-of-the-art ML models.

Technologies such as machine learning can achieve accurate personalized recommendations. However, due to the collection and utilization of a large amount of user information in this process, people are widely worried about data security and privacy issues. This paper first introduces two key issues of privacy protection in the field of machine learning, namely data privacy and model privacy. On this basis, this paper introduces and analyzes homomorphic encryption, differential privacy and federated learning, and compares their advantages and disadvantages. Among them, homomorphic encryption technology has a large computational cost, differential privacy technology has a negative impact on system accuracy, and federated learning technology has a high training and communication cost. Therefore, it will be the future research direction to study more efficient and accurate recommendation models.

PPFLHE: A privacy-preserving federated learning scheme with homomorphic encryption for healthcare data