The use of vehicle ad hoc networks (VANET) is increasing, VANET is a network in which two or more vehicles communicate with each other. The VANET architecture is vulnerable to various attacks, such as DoS and DDoS attacks hence various strategies were previously employed to combat these attacks, but the presence of end-to-end transparency and N-to-1 mapping of different IP addresses create failure in the blockage and not able to determine the twelve variants of DDoS attacks hence a novel technique, Encrypted Access Hex-tuple Mapping Attack detection was proposed, which uses triple random hyperbolic encryption, which performs triple random encoding to encrypt traffic signals and obtains the public key by plotting random values in hyperbola to strengthen the access control in the middlebox and Deep auto sparse impasse NN is used to detect twelve variant DDoS attacks in the VANET architecture. Moreover, to provide immunity against attack, the existing approach uses various artificial immune systems to prevent DDoS attacks but the selection of positive and negative clusters generates too many indicator packets. Hence a novel technique, Stable Automatic Optimized Cache Routing proposed, which uses a Deep trust factorization NN to detect irrational nodes without requiring prior negotiation about local outliner factor and direct evidence by automatically extracting trust factors of each node to manage the packet flows and detecting transmission of dangerous malware files in the network to prevent various types of hybrid DDoS attacks at VANET architecture. The proposed model is implemented in NS-3 to detect and prevent hybrid DDoS attacks. 

Vehicle ad hoc networks, or VANETs, are highly mobile wireless networks created to help with traffic monitoring and vehicular safety. Security risks are the main problems in VANET. To handle the security threats and to increase the performance of VANETs, this paper proposes an enhanced trust based aggregate model. In the proposed system, a novel adaptive nodal attack detection approach - entropy-based SVM with linear regression addresses the trust factor with kernel density estimation generating the trustiness value thereby classifying the malicious nodes against the trusted nodes in VANETs. Defending the VANETs is through a novel reliance node estimation approach - Bayesian self-healing AIS with Pearson correlation coefficient aggregate model isolating the malicious node thereby the RSU cluster communication getting secure. Furthermore, even a reliable node may be exploited to deliver harmful messages and requires the authority of both the data and the source node to be carried out by the onboard units of the vehicles getting the reports of incident. DoS attacks (Denial of Service) disrupting the usual functioning of the network leads to inaccessible network to its intended users thereby endangering human lives. The proposed system is explicitly defending the VANET against DoS attacks as it predicts the attack without compromising the performance of the VANET handling nodes with various features and functions based on evaluating the maliciousness of attacking nodes accurately and isolating the intrusion. Furthermore, the performance evaluations prove the effectiveness of the proposed work with increased detection rate by 97%, reduced energy consumption by 39% and reduced latency by 25% compared to the existing studies.