Deep Belief Networks for Feature Learning in VANET Security Analysis

Past few decades have witnessed large increase in number of vehicles on roads due to which the traffic safety needs have also become complex and hence necessary to be taken care of. Vehicular ad hoc networks (VANETs) are becoming an asset for achieving all these goals. VANETs provide applications like driver assistance, passenger comfort and vehicle safety. Apart from its general characteristics due to the dynamic nature of road environments and traffic patterns, communication linkage among vehicles in VANETs suffers breakage problem hence requires a very reliable routing protocol. To manage the communication efficiently many routing protocols are already available for MANETs which can be further used for routing and communication among vehicles in VANETs, but characteristics of these two networks (VANET and MANET) are very different, so there is requirement to enhance these MANET routing protocols so that those can be used in VANETs. When we use some existing routing protocols in VANET then number of link breakage are surprisingly high because mobility is higher in VANETs as compared to MANETs. So this paper presents advanced version of an existing MANET routing protocol for better performance in VANET environment. The new routing protocol has removed problem of large number of routing error messages which was existing in AODV routing protocol. Protocol proposed in this paper has better results as compared to AODV and paper has also shown results of simulation and also elaborated the findings.

Precision mechanism is widely used for various industry applications. Quality inspection for precision mechanism is essential for manufacturers to assure the product leaving factory with expected quality. In this paper, we propose a novel automated fault detection method, named Tilear, based on a Deep Belief Network (DBN) auto-encoder. DBN is a probabilistic generative model, composed by stacked Restricted Boltzmann Machines. With its RBM-layer-wise training methods, DBN can perform fast inference and extract high level feature of the inputs. By unfolding the stacked RBMs symmetrically, a DBN auto-encoder is constructed to reconstruct the inputs as closely as possible. Based on the DBN auto-encoder, Tilear is structured in two parts: training and decision-making. During training, Tilear is trained with the signals only from good samples, which enables the trained DBN auto-encoder only know how to reconstruct signals of good samples. In the decision-making part, comparing the recorded signal from test sample and the Tilear reconstructed signal, allows to measure how well a recording from a test sample matches the DBN auto-encoder model learned from good samples. A reliable decision could be made. We perform experiments on two different precision mechanisms: precision electromotors and greasing control units. The feasibility of Tilear was demonstrated first. Additionally, performance of Tilear on the acquired electromotor dataset was compared with the state-of-the-art machine learning based fault detection technique, support vector machine (SVM). First result indicates that Tilear excels the SVM in terms of the Area Under the Curve (AUC) obtained from the Receiver Operating Characteristics (ROC) curve plot: 0.960 achieved by Tilear, while 0.941 by SVM.

Context: Vehicular Ad-hoc network (VANET) is an emerging trend that originates from the field of Mobile Adhoc Network that gained significant attention from the past few years. The communication is realized through the On-Board Unit (OBU) equipped in vehicles. The concern of VANET is to provide a safe journey to the passengers. Although its significant high mobility of vehicles may cause a loss in connection or security attacks. Objective: The aim behind systematic literature review (SLR) is to identify the diverse type of security attacks in VANET and their respective solutions to handle those attack. This study mainly focuses on VANET centric security attacks and categorizes them along with the solutions to handle these attacks. Method: By using kitchenham guideline for systematic review and PRISMA flow for study selection to ensure coverage of all relevant studies we initially came up with 403 articles. Among these studies, 84 studies were qualified to be relevant according to 4 defined research questions. After identification and categorization of VANET centric attacks, proposed solutions a mapping between attacks and solution is provided. Results: Result of this systematic literature review suggest that identifies Access Management is a major concern in VANET with 44% of selected studies followed by Privacy 28% and Sybil attack 5%. Similarly, numerous security concerns such as Message Authentication 3%, Trust Management 4% and Black Hole attack 3% receives considerably lesser attention by the research community. Conclusions: In the context of security-related solutions which technique found to be most widely used to resolve the problem. The contribution of this study is considered important because there is no such study that has provided such an extensive overview of VANET security attacks and their perspective solutions.

The integration of 5G and VANET will result in an intelligent transportation system. Relay of 5G data from mobile networks to VANET which is greatly underutilized can reduce the burden on mobile networks. Wireless technology has been used in vehicles or better known as the Vehicular Ad hoc Network (VANET). One of the keys to the success of sending data on VANET is the Path Selection Algorithm which is the task of the routing protocol. The routing protocol that is widely used in research in the VANET environment is AODV (Adhoc On-Demand Distance Vector), because among other protocols, AODV is the most suitable and suitable for implementation in the VANET environment, and AODV is also one of the reactive routing protocols. This study includes an increase in the performance of a routing protocol, so in this study, the SIN (Static Intersection Node) is added to the RSU (Road-Side Unit) which is located at an intersection. Which functions as a repeater that helps send data packets to other vehicles around it so that it can increase the Packet Delivery Ratio and minimize Packet Loss and End to End Delay. The focus of this research is to determine the most ideal Static Intersection Node position as well as the most effective number of Static Intersection Nodes.

A survey on authentication schemes in VANETs for secured communication

A type of ad hoc network formed among moving vehicles that come in one another’s radio transmission range is called Vehicular Ad Hoc Network (VANET). VANET may be deployed using three architectures: pure ad hoc, WLAN and hybrid. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) are the two modes in VANET used for communication among vehicles. These features not only distinguish a VANET from other ad hoc networks but also make these networks more exposed to attacks and increase their complexity. These networks being the primary mechanism for communication in VANETs, appropriate and timely delivery of information is of prime importance. Due to the existence of various vehicular traffic scenarios, a single category of routing protocols is not sufficient for the VANETs. Therefore, various categories of routing protocols have been tailored to meet specific kinds of routing requirements in this framework. Most significant ones are ad hoc/topology-based, position-based, geocast-based, cluster-based, broadcast-based protocols. There exist a number of attacks that apart from affecting various other parameters also affect the routing protocols in these VANETs. Most of these attacks may be launched in all the above-mentioned three architectures. But, one of the most dangerous attacks is the Sybil attack that may be initiated in pure ad hoc deployment of VANET where vehicles communicate with one another in one to one manner using carry forward approach. In this paper, with the help of illustrative example for each of the five categories of routing protocols, we show how Sybil attack affects these protocols. Considering the ad hoc scenarios in VANETs, we also discuss a prevention mechanism for the Sybil attack briefly.

Vehicular Ad-Hoc Networks (VANET) are a proper subset of mobile wireless networks, where nodes are revulsive, the vehicles are armed with special electronic devices on the motherboard OBU (On Board Unit) which enables them to trasmit and receive messages from other vehicles in the VANET. Furthermore the communication between the vehicles, the VANET interface is donated by the contact points with road infrastructure. VANET is a subgroup of MANETs. Unlike the MANETs nodes, VANET nodes are moving very fast. Impound a permanent route for the dissemination of emergency messages and alerts from a danger zone is a very challenging task. Therefore, routing plays a significant duty in VANETs. decreasing network overhead, avoiding network congestion, increasing traffic congestion and packet delivery ratio are the most important issues associated with routing in VANETs. In addition, VANET network is subject to various security attacks. In base VANET systems, an algorithm is used to dicover attacks at the time of confirmation in which overhead delay occurs. This paper proposes (P-Secure) approach which is used for the detection of DoS attacks before the confirmation time. This reduces the overhead delays for processing and increasing the security in VANETs. Simulation results show that the P-Secure approach, is more efficient than OBUmodelVaNET approach in terms of PDR, e2e_delay, throughput and drop packet rate.

This chapter presents the emerging security issues in Vehicular Ad hoc Networks (VANETs) for e-business along with some of the solutions provided by the research community. The VANET will facilitate new applications for e-business that will revolutionize the driving experience, providing everything from instant, localized traffic updates to warning signals when the vehicle ahead abruptly brakes. In the emerging global economy, e-business has increasingly become a necessary component of business strategy and a strong catalyst for economic development. In near future, vehicles may be equipped with short-range radios capable of communicating with other vehicles and highway infrastructure using a VANET. However, providing security in VANETs for e-business raises privacy concerns that must be considered. The deployment of VANETs for e-business is rapidly approaching, and their success and safety will depend on viable security solutions acceptable to consumers, manufacturers and governments.

Vehicular Ad hoc Networks (VANET) broadcast messages regarding road and environmental conditions. Due to its design, VANET inadvertently introduced security and privacy issues. Many researchers have suggested various approaches to address these shortcomings as the deployment of VANET becomes more widespread. Nevertheless, these solutions could not address all the security and privacy shortcomings in VANET. Furthermore, the proposed approaches incur high costs in terms of computation due to the complexity involved in doing so sequentially. One of the significant approaches used in VANET security and privacy mitigation is identity-based schemes. This paper provides a comprehensive survey on VANETs and the entities involved, attack models, and an analysis of the security and privacy requirements for identity-based security and privacy schemes for VANETs.

Software Defined Network (SDN) is getting popularity both from academic and industry. Lot of researches have been made to combine SDN with future Internet paradigms to manage and control networks efficiently. SDN provides better management and control in a network through decoupling of data and control plane. Named Data Networking (NDN) is a future Internet technique with aim to replace IPv4 addressing problems. In NDN, communication between different nodes done on the basis of content names rather than IP addresses. Vehicular Ad-hoc Network (VANET) is a subtype of MANET which is also considered as a hot area for future applications. Different vehicles communicate with each other to form a network known as VANET. Communication between VANET can be done in two ways (i) Vehicle to Vehicle (V2V) (ii) Vehicle to Infrastructure (V2I). Combination of SDN and NDN techniques in future Internet can solve lot of problems which were hard to answer by considering a single technique. Security in VANET is always challenging due to unstable topology of VANET. In this paper, we merge future Internet techniques and propose a new scheme to answer timing attack problem in VANETs named as Timing Attack Prevention (TAP) protocol. Proposed scheme is evaluated through simulations which shows the superiority of proposed protocol regarding detection and mitigation of attacker vehicles as compared to normal timing attack scenario in NDN based VANET.

Vehicular ad hoc networks (VANET) are emerging as a prominent form of mobile ad hoc networks (MANETs) and as an effective technology for providing a wide range of safety applications for vehicle passengers. Nowadays, VANETs are of an increasing importance as they enable accessing a large variety of ubiquitous services. Such increase is also associated with a similar increase in vulnerabilities in these inter-vehicular services and communications, and consequently, the number of security attacks and threats. It is of paramount importance to ensure VANETs security as their deployment in the future must not compromise the safety and privacy of their users. The successful defending against such VANETs attacks prerequisite deploying efficient and reliable security solutions and services, and the research in this field is still immature and is continuously and rapidly growing. As such, this paper is devoted to provide a structured and comprehensive overview of the recent research advances on VANETS security services, surveying the state-of-the-art on security threats, vulnerabilities and security services, while focusing on important aspects that are not well-surveyed in the literature such as VANET security assessment tools.

In order to extract the features of the image more accurately, a deep belief network (DBN) based image feature extraction method is proposed. However, when the deep belief network extracts the features of the image, it is easy to ignore the local texture features of the image. Then the block local local binary mode is introduced to extract the local texture features of the image. At the same time, to improve the slow learning speed of the network, the initial weight of the network is improved. Finally, the proposed network is tested on the ORL image dataset. The results show that the proposed method not only improves the recognition accuracy of the network, but also accelerates the convergence speed of the network to some extent.

As urban populations grow, cities face increasing challenges in developing transportation systems that are both efficient and secure. Vehicular Ad Hoc Networks (VANETs) provide a promising solution by enabling real-time communication between vehicles and infrastructure, improving road safety and optimizing traffic flow. However, VANETs encounter issues related to data integrity, privacy, and scalability. The integration of blockchain technology offers a secure, decentralized framework for data sharing and identity verification. This chapter introduces a blockchain-enhanced VANET architecture that tackles these challenges by, Proof-of-Work (PoW) consensus for VANETs, enabling vehicles and infrastructure nodes to share computational efforts with the help of collabrative mining. This approach addresses the resource constraints and real-time requirements typical in VANET environments. enhancing security, reducing traffic delays, and improving system resilience, offering a transformative approach to urban mobility.

The exploration of Vehicular Ad Hoc Networks (VANETs) has garnered attention from scholars and businesses alike because of their changing structure and expected traffic flow patterns. VANETs offer an avenue to boost road safety by offering drivers up-to-the-minute updates and predictions regarding road conditions. As a version of Mobile Ad Hoc Networks (MANETs) VANETs consist of units that can communicate wirelessly and share information. This study explores elements of VANET (Vehicular Ad Hoc Networks) such, as structures and functions of networks connection points and the issues and methods used for guiding traffic efficiently to create a secure and dependable communication network system. Key indicators of performance like how many packets delivered (Packet Delivery Ratio. PDR) packet losses experienced during transmission processes are carefully analyzed to gauge the overall reliability of the system. Additionally, the research includes a comparison among routing strategies employed in VANET environments such as those based on network topology information or geographical location details, versus approaches centered around organized clusters of vehicles. Highlighting how these differ from methodologies typically seen in traditional cellular networks. This paper provides insights into how different routing protocols work in networking paradigms by examining different perspectives.

Estimating emotional states in music listening based on electroencephalogram (EEG) has been capturing the attention of researchers in the past decade. Although deep belief network (DBN) has witnessed the success in various domains including early works in emotion recognition based on EEG, it remains unclear whether DBN could improve emotion classification in music domains, especially in dynamic strategy that considers time-varying characteristics of emotion. This paper presents an early study of applying DBNs to improve emotion recognition in music listening where emotions were annotated continuously in time by subjects. Our subject-dependent results using stratified 10-fold cross-validation strategy suggested that DBNs could improve performance in valence classification with fractal dimension (FD), power spectral density (PSD), and discrete wavelet transform (DWT) features and improve performance in arousal classification with FD and DWT features. Furthermore, we found that the size of sliding window affected classification accuracies when using features in time (FD) and time-frequency (DWT) domains, while smaller window (1–4 seconds) could achieve higher performance compared with a larger window (5–8 seconds).