Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average value of classes as the new clustering center on the basis of random selection, merging and dividing dynamically, then finish the clustering procedure through the iteration finally. Experimental results with DARPA1999 testing data set show that the clustering method is more excellent than traditional clustering methods in both aggregation rate and error aggregation rate. Besides, it reduces duplicated alarm effectively and provides assistance to further related work

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely, Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when artificial intelligence (AI) technology has been introduced into intrusion detection study. This article first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems. Finally, we discuss the open challenges and future research directions.

Influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.

Emerging as the effective strategy of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) have the capacity of drastically improving the driving experience and road safety. In typical VANET scenarios, high mobility and volatility of vehicles result in dynamic topology of vehicular networks. That is, individual vehicle may pass through the effective domain of multiple neighboring road-side-units (RSUs) during a comparatively short time interval. Hence, efficient and low-latency cross-domain verification with all the successive RSUs is of significance. Recently, a lot of research on VANET authentication and key distribution was presented, while the critical cross-domain authentication (CDA) issue has not been properly addressed. Particularly, the existing CDA solutions mainly reply on the acquired confidential keying information from the neighboring entities (RSUs and vehicles), while too much trustworthiness is granted to the involved RSUs. Please note that the RSUs are distributively located and may be compromised or disabled by adversary, thus vital vehicle information may be revealed. Furthermore, frequent data interactions between RSUs and cloud server are always the major requisite so as to achieve mutual authentication with cross-domain vehicles, which leads to heavy bandwidth consumption and high latency. In this paper, we address the above VANET cross-domain authentication issue under the novel RSU edge networks assumption. Please note that RSUs are assumed to be semi-trustworthy entity in our design, where critical vehicular keying messages remain secrecy. Homomorphic encryption design is applied for all involved RSUs and vehicles. In this way, successive RSUs could efficiently verify the cross-domain vehicle with the transited certificate from the neighbor RSUs and vehicle itself, while the identity and secrets of each vehicle is hidden all the time. Afterwards, dynamic updating towards the anonymous vehicle identity is conducted upon validation, where conditional privacy preserving is available. Moreover, pairing-free mutual authentication method is used for efficiency consideration. Formal security analysis is given, proving that the HCDA mechanism yields desirable security properties on VANET cross domain authentication issue. Performance discussions demonstrate efficiency of the proposed HCDA scheme compared with the state-of-the-art.

With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system.

In real social networks, it is often the case that opposite opinions, ideas, products or innovations are propagating simultaneously. Although the competitive influence problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-aware influence blocking maximization (LIBM) problem, which aims to find a positive seed set to maximize the blocked negative influence for a given query region. In order to overcome low efficiency of the greedy algorithm, we propose two heuristic algorithms LIBM-H and LIBM-C based on the quadtree index and the maximum influence arborescence (MIA) structure. Experimental results on real-world datasets show that both LIBM-H and LIBM-C are able to achieve matching blocking effect to the greedy algorithm and often better than other heuristic algorithms, whereas they are several orders of magnitude faster than the greedy algorithm.