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Generalized projective synchronization of two coupled complex networks of different sizes *Li Ke-Zan(李科赞) a) † , He En(何 恩) a) , Zeng Zhao-Rong(曾朝蓉) b) , and Chi K. Tse(谢智刚) c)

Open TCP/UDP ports are traditionally used by servers to provide application services, but they are also found in many Android apps. In this paper, we present the first openport analysis pipeline, covering the discovery, diagnosis, and security assessment, to systematically understand open ports in Android apps and their threats. We design and deploy a novel ondevice crowdsourcing app and its server-side analytic engine to continuously monitor open ports in the wild. Over a period of ten months, we have collected over 40 million port monitoring records from 3,293 users in 136 countries worldwide, which allow us to observe the actual execution of open ports in 925 popular apps and 725 built-in system apps. The crowdsourcing also provides us a more accurate view of the pervasiveness of open ports in Android apps at 15.3%, much higher than the previous estimation of 6.8%. We also develop a new static diagnostic tool to reveal that 61.8% of the open-port apps are solely due to embedded SDKs, and 20.7% suffer from insecure API usages. Finally, we perform three security assessments of open ports: (i) vulnerability analysis revealing five vulnerability patterns in open ports of popular apps, e.g., Instagram, Samsung Gear, Skype, and the widely-embedded Facebook SDK, (ii) inter-device connectivity measurement in 224 cellular networks and 2,181 WiFi networks through crowdsourced network scans, and (iii) experimental demonstration of effective denial-of-service attacks against mobile open ports.

Summary Recently, some existing works have introduced novel way to construct complex networks from embedded time series, which provides new sights into the organizational properties of the time series in phase space. In this paper, we attempt to answer the fundamental question of “how much information regarding the dynamic property of the original time series can be extracted from these networks.” To this end, we propose a new method for reconstructing time series from the networks. We compare the reconstructed time series from these networks and that from the recurrence plot. We find that these networks contain topological information of the embedded time series to a certain degree. In general, they are more powerful than the recurrence plot method in the reconstruction of embedded time series. In addition, we study a new generalized projective synchronization (GPS) of coupled complex dynamical networks with different sizes via feedback control and impulsive control. Based on the stability analysis of impulsive system, a network synchronization criterion is established. These works may find potential application for secure communication via networks.

Late in 2019, the unique viral disease coronavirus disease, or COVID-19, initially appeared. On March 11, 2020, the World Health Organization (WHO) proclaimed the COVID-19 outbreak a pandemic. It rapidly spread to every corner of the globe. This paper examines the use and actual application of big data in epidemic early warning. Based on the analysis of the value of big data epidemic early warning mechanisms, this paper divides the current big data epidemic early warning systems into three main categories according to the various channels of data acquisition: early warning systems based on the Internet and communication systems, early warning systems based on electronic medical information, and early warning mechanisms based on the Internet of Things information collection.