Dynamic monitoring the infrastructure of major ports in Sri Lanka by using multi-temporal high spatial resolution remote sensing images

Colombo port and Hambantota port in Sri Lanka play a key role in transiting and supporting the shipping trade of ＂the 21 st-Century Maritime Silk Road＂. In recent years, Chinese enterprises have made huge investments in the infrastructure construction of Colombo port and Hambantota port. The construction progress and development trend of Colombo port and Hambantota port have been attracting the attention of Chinese investment enterprises and the society. In this paper, multi-temporal high spatial resolution remote sensing images are used to monitor the infrastructure construction condition of Colombo port and Hambantota port from 2010 to 2017. According to the interpreted infrastructure information of the two ports, the international container terminal of Colombo and Hambantota port have completed their constructions. By the end of 2017, the international container terminal of Colombo built the container yards with 28.8 ha and roads with 32.6 ha. At the south of the international container terminal of Colombo, the 62.2 ha of reclamation area were built for the planned port city. In Hambantota port, 77 ha of container yards, 48 ha of roads and 2.9 ha of oil storage areas were constructed during this period. Meanwhile, the analysis of potential storage capacity of Colombo port and Hambantota port shows that the throughput of Colombo port may increase by 3 million tons per year while the throughput of Hambantota port will be over its designed 2.5 million tons per year. These analysis results are able to provide a useful reference for Chinese investment enterprises and the related research of ＂the Belt and Road＂.

A lightweight DDoS detection scheme under SDN context

Software-defined networking(SDN),a novel network paradigm,separates the control plane and data plane into dif-ferent network equipment to realize the flexible control of network traffic.Its excellent programmability and global view present many new opportunities.DDoS detection under the SDN context is an important and challenging research field.Some previous works attempted to collect and analyze statistics related to flows,usually recorded in switches,to address DDoS threats.In contrast,other works applied machine learning-based solutions to identify DDos and achieved promising results.Generally,most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions.Nevertheless,the request for flow rules is very time-consuming and CPU-consuming;moreover may congest the communication channel between the controller and the switches.Therefore,we present FORT,a lightweight DDoS detection scheme,which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state.A time-series algorithm,ARIMA,is utilized to determine the port statistics adaptively,and an SVM algorithm is applied to detect whether a DDoS attack does occur.Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy.Referring to the false alarm rate of 1.24%in the comparison scheme,the false alarm rate of this scheme is only 0.039%,which significantly reduces the probability of false alarm.Besides,by introducing the alarm mechanism,this scheme can reduce the load of the southbound chan-nel by more than 60%in the normal state.