Vehicular ad hoc networks (VANETs) enable wireless communication among Vehicles and Infrastructures. Connected vehicles are promising in Intelligent Transportation Systems (ITSs) and smart cities. The main objective of VANET is to improve the safety, comfort, driving efficiency and waiting time on the road. VANET is unlike other ad hoc networks due to its unique characteristics and high mobility. However, it is vulnerable to various security attacks due to the lack of centralized infrastructure. This is a serious threat to the safety of road traffic. The Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle parts simultaneously. The message moves through CAN bus from one node to another node, but it does not have information about the source and destination address for authentication. Thus, the attacker can easily inject any message to lead to system faults. In this paper, we present machine learning techniques to cluster and classify the intrusions in VANET by KNN and SVM algorithms. The intrusion detection technique relies on the analysis of the offset ratio and time interval between the messages request and the response in the CAN.

Abstract-This paper presents an efficient technique for solving a Boolean matching problem in cell-library binding, where the number of cells in the library is large. As a basis of the Boolean matching, we use the notion NP-representative (NPR); two functions have the same NPR if one can be obtained from the other by a permutation and/or complementation(s) of the variables. By using a table look-up and a tree-based breadthfirst search strategy, our method quickly computes NPR for a given function. Boolean matching of the given function against the whole library is determined by checking the presence of its NPR in a hash table, which stores NPRs for all the library functions and their complements. The effectiveness of our method is demonstrated through experimental results, which shows that it is more than two orders of magnitude faster than the Hinsberger-Kolla's algorithm-the fastest Boolean matching algorithm for large libraries.

Abstract-An AND-OR-EXOR network, where the output EXOR gate has only two inputs, is one of the simplest three-level architecture. This network realizes an EXOR of two sum-of-products expressions (EX-SOP). In this paper, we show an algorithm to simplify EX-SOPs for multipleoutput functions. Our objective is to minimize the number of distinct products in the sum-of-products expressions of EX-SOPs. The algorithm uses a divide-and-conquer strategy. It recursively applies the Shannon decomposition on a function with more than five variables. The algorithm obtains EX-SOPs for the five-variable functions by using an exact minimization program, then combines those EX-SOPs to generate EX-SOPs for the functions with more variables. We present experimental results for a set of benchmark functions, and show that EX-SOPs require many fewer products and literals than sum-of-products expressions. This is evidence that AND-OR-EXOR is a powerful architecture to realize many practical logic functions. Index Terms-Three-level network, AND-EXOR, logic minimization, decomposition, programmable logic device.

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Design methods for OR-AND-OR three-level networks are useful for exploiting the flexibility of logic blocks in many complex programmable logic devices. This paper presents TRIMIN, a fast heuristic algorithm for designing OR-AND-OR networks from sum-of-products expressions. Each output of the network realizes a sum-of-complex-terms expression, where a complex term (CT) is similar to a product-of-sums expression. TRIMIN's objective is to lower the number of gates in the network. It first generates a set of CTs by applying factorization techniques; then, it solves a set-covering problem by using a greedy algorithm to select a subset of the CTs. The effectiveness of TRIMIN is demonstrated through experimental results.

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