Biblio

Underwater acoustic sensor networks (UASNs) have been receiving more and more attention due to their wide applications and the marine data collection is one of the important applications of UASNs. However, the openness and unreliability of underwater acoustic communication links and the easy capture of underwater wireless devices make UASNs vulnerable to various attacks. On the other hand, due to the limited resources of underwater acoustic network nodes, the high bit error rates, large and variable propagation delays, and low bandwidth of acoustic channels, many mature security mechanisms in terrestrial wireless sensor networks cannot be applied in the underwater environment [1]. In this paper, a secure communication protocol for marine data collection was proposed to ensure the confidentiality and data integrity of communication between under sensor nodes and the sink node in UASNs.

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model—multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater that can reveal unobserved dependencies with linear complexity. Moreover, we derive F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater-ZERO, an online variant of F\textbackslashtextlessscp;\textbackslashtextgreaterascinate\textbackslashtextless/scp;\textbackslashtextgreater that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.