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Crowded scene surveillance can significantly benefit from combining egocentric-view and its complementary top-view cameras. A typical setting is an egocentric-view camera, e.g., a wearable camera on the ground capturing rich local details, and a top-view camera, e.g., a drone-mounted one from high altitude providing a global picture of the scene. To collaboratively analyze such complementary-view videos, an important task is to associate and track multiple people across views and over time, which is challenging and differs from classical human tracking, since we need to not only track multiple subjects in each video, but also identify the same subjects across the two complementary views. This paper formulates it as a constrained mixed integer programming problem, wherein a major challenge is how to effectively measure subjects similarity over time in each video and across two views. Although appearance and motion consistencies well apply to over-time association, they are not good at connecting two highly different complementary views. To this end, we present a spatial distribution based approach to reliable cross-view subject association. We also build a dataset to benchmark this new challenging task. Extensive experiments verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Network flows are the basic components of the Internet. Considering the serious consequences of abnormal flows, it is crucial to provide timely anomaly detection in dynamic communication networks. To obtain accurate anomaly detection results in dynamic networks, supervision from experts is highly demanded. However, to obtain high-quality ground truth of abnormal flows, we suffer from two major problems: (1) limited labor resources: experts with the latest domain knowledge are much fewer than the large number of flows; and (2) dynamic environment: considering the new abnormal patterns (i.e., new attacks) and continuously changing network structures, it requires timely supervision to adaptively update the parameters. To tackle these problems, we propose HADDN, a novel bandit framework for periodic-updated anomaly detection in dynamic communication networks. We formulate the task as a bandit problem, where by interactions, supervision is offered by human experts to provide the ground truth to a fraction of flows. We construct semi-parametric expected rewards to optimize the estimation of flows’ abnormality in limited interactions. Also, we utilize feature-based clusters and structural correlations to make connections between historical flows and new flows to improve both efficiency and accuracy of abnormality estimation. What’s more, we provide two implementations for the semi-parametric expected reward of the proposed HADDN with theoretical proof. Experimental evaluations on public datasets demonstrate the substantial improvement of our proposed approaches compared to state-of-art anomaly detection methods. [ABSTRACT FROM AUTHOR]

By moving computing resources close to where they are needed (i.e., the network edges), edge computing can significantly reduce burden on the centric cloud data centers. However, extreme scale of on-line big data may impose a significant burden on the network backbones. Information-centric edge networking can address this challenge by incorporating in-network caching into edge networks. This however, opens a door for many new security issues and requires various security defenses. One of those is efficient access control design specifically for information-centric edge networking. In this work, we aim to design an efficient and secure access control scheme for information-centric edge networking. In our design, we propose the confidentiality-enhanced network coding which can ensure that, without having access to the authorization key, the attacker will not be able to obtain the original content. And thanks to the properties of confidentiality-enhanced network coding, highly efficient access control can be realized by encrypting only part of the encoding matrix. In addition, our design can allow efficiently revoking users. Security analysis and experimental evaluation on NS3 demonstrate that our scheme can successfully enforce access control in information-centric edge networking with a small overhead. [ABSTRACT FROM AUTHOR]

Authentication in a vehicular ad-hoc network (VANET) requires not only secure and efficient authentication with privacy preservation but applicable flexibility to handle complicated transportation circumstances as well. In this paper, we proposed a Two-Factor LIghtweight Privacy-preserving authentication scheme (2FLIP) to enhance the security of VANET communication. 2FLIP employs the decentralized certificate authority (CA) and the biological-password-based two-factor authentication (2FA) to achieve the goals. Based on the decentralized CA, 2FLIP only requires several extremely lightweight hashing processes and a fast message-authentication-code operation for message signing and verification between vehicles. Compared with previous schemes, 2FLIP significantly reduces computation cost by 100–1000 times and decreases communication overhead by 55.24%–77.52%. Furthermore, any certificate revocation list (CRL)-related overhead on vehicles is avoided. 2FLIP makes the scheme resilient to denial-of-service attack in both computation and memory, which is caused by either deliberate invading behaviors or jammed traffic scenes. The proposed scheme provides strong privacy preservation that the adversaries can never succeed in tracing any vehicles, even with all RSUs compromised. Moreover, it achieves strong nonrepudiation that any biological anonym driver could be conditionally traced, even if he is not the only driver of the vehicle. Extensive simulations reveal that 2FLIP is feasible and has an outstanding performance of nearly 0-ms network delay and 0% packet-loss ratio, which are particularly appropriate for real-time emergency reporting applications. [ABSTRACT FROM PUBLISHER]