We live in the world of "Internet of Everything", which lead to advent of different applications and Internet of vehicles (IOV) of one among them, which is a major step forward for the future of transportation system. Vehicle to vehicle (V2V) communication plays a major role in which a vehicle may send sensitive, non-sensitive messages and these messages are encrypted with public keys, which makes distribution of public keys is a major problem due to the vehicle need to be anonymous having pseudonyms which changes more frequently and makes it more complicated. Here we proposed a hybrid approach, which uses existing Public key certificate for authorization of the vehicle and Identity Based Encryption to generate public keys from the pseudonyms and use it in secure V2V communication without compromising anonymity of the vehicle.

Brave new world was emerging and chancing its face day by day with the advent of IOT, as a part of it we have IOV which may change the way we are driving our vehicles leading to Autonomous driving, As there is a lot of Buzz on these trends they also comes with security threats of different forms in which Sybil Attack is one in which a malicious vehicle may create multiple identities to a Real one, with which he may circumvent different forms of attacks, We proposed an approach in which we divide the vehicles in to different clusters with their location information and certificate and filter the Sybil nodes with an idea no two nodes with the same identities will have different location ID's or may fall in different clusters at the same time.

IOV is a unique form of VANETS network with a special kind of remote system which is arranged with set of versatile vehicles that can be effortlessly included and expelled from the system with no incorporated organization where each node acts as both sender / receiver and a Hub which forwards the packets. Owing to absence of centralized administration, Nodes in IOV are exposed to various network layer attacks. Among them, the Wormhole attack is one of its kind of attack and must to be addressed with high attention. In this paper, we propose and implement a new algorithm to detect the tunneling attack. The performance of the proposed algorithm is evaluated against various existing algorithms and a comparison is made with our proposed Wormhole Path Watcher Packet (WPWP) technique using NS2 tool.

Frequent Itemset Mining become so popular in extracting hidden patterns from transactional databases. Among the several approaches, Apriori algorithm is known to be a basic approach which follows candidate generate and test based strategy. Although it is efficient level-wise approach, it has two limitations, (i) several passes are required to check the support of candidate itemsets. (ii) Towards more candidate itemsets and minimum threshold variations. A novel approach is proposed to tackle the above limitations. The proposed approach is one pass Hash-based Frequent Itemset Mining to derive frequent patterns. HFIM has feature that maintains candidate itemsets dynamically which are independent on minimum threshold. This feature allows to limit the number of scans over the database to one. In this paper, HFIM is compared with the Apriori to show the performance on standard datasets. The result section shows that HFIM outperforms Apriori over large databases.