High-Risk LEO Satellite Network Path Detection Based on Spatial and Temporal Delay Anomaly Analysis

The gradual deployment of Low-Earth Orbit(LEO)mega constellations with inter-satellite links(ISLs)promises ubiquitous,low-latency,and high-throughput satellite network services.However,networked LEO satellites with ISLs are also at risk of routing attacks such as hijacking.Existing defenses against route hijacking in terrestrial networks can hardly work for the LEO satellite network due to its high spatiotemporal dynamics.To deal with it,we propose RPD,a high-risk routing path detection method for LEO mega-constellation networks.RPD detects abnormal high-risk LEO network paths by checking the consistency between the path delay and the geographical distance.This is efficiently achieved by combining in-band measurements and out-of-band statistical processing to detect the anomaly of the clustering feature in the reference delay matrix.RPD avoids the recalculation of the header cryptographic marks when the handover occurs,thus greatly reducing the cost and improving the performance of highrisk path detection.Experiments showed that the proposed RPD mechanism achieves an average detection accuracy of 91.64%under normal network conditions,and maintain about 89%even when congestion occurs in multiple areas of the network and measurement noise is considered.In addition,RPD does not require any cryptographic operation on the intermediate node,only minimal communication cost with excellent scalability and deployability.

ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

With the increasing number of switches in Software-Defined Network-ing(SDN),there are more and more faults rising in the data plane.However,due to the existence of link redundancy and multi-path forwarding mechanisms,these problems cannot be detected in time.The current faulty path detection mechan-isms have problems such as the large scale of detection and low efficiency,which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN.Concerning this issue,we propose an efficient network path fault testing model ProbD based on probability detection.This model achieves a high prob-ability of detecting arbitrary path fault in the form of small-scale random sam-pling.Under a certain path fault rate,ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times.After a small number of experiments,the ProbD model can cor-rectly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network.Thefinal experimental results show that,compared with the full path coverage test,the ProbD model based on probability detection can achieve efficient network testing with less overhead.Besides,the larger the network scale is,the more overhead will be saved.

Principal-component estimates of the Kuroshio Current axis and path based on the mathematical verification between satellite altimeter and drifting buoy data

We used satellite altimetry data to investigate the Kuroshio Current because of the higher resolution and wider range of observations. In previous studies, satellite absolute geostrophic velocities were used to study the spatiotemporal variability of the sea surface velocity field along the current, and extraction methods were employed to detect the Kuroshio axes and paths. However, sea surface absolute geostrophic velocity estimated from absolute dynamic topography should be regarded as the geostrophic component of the actual surface velocity, which cannot represent a sea surface current accurately. In this study, mathematical verification between the climatic absolute geostrophic and bin-averaged drifting buoy velocity was established and then adopted to correct the satellite absolute geostrophic velocities. There were some differences in the characteristics between satellite geostrophic and drifting buoy velocities. As a result, the corrected satellite absolute geostrophic velocities were used to detect the Kuroshio axis and path based on a principal-component detection scheme. The results showed that the detection of the Kuroshio axes and paths from corrected absolute geostrophic velocities performed better than those from satellite absolute geostrophic velocities and surface current estimations. The corrected satellite absolute geostrophic velocity may therefore contribute to more precise day-to-day detection of the Kuroshio Current axis and path.

An optical sensor for hydrogen sulfide detection in open path using WMS-2f/1f technique

An optical hydrogen sulfide(H_2S) sensor based on wavelength modulation spectroscopy with the second harmonic(2f) corrected by the first harmonic(1f) signal(WMS-2f/1f) is developed using a distributed feedback(DFB) laser emitting at 1.578 μm and a homemade gas cell with 1-m-long optical path length. The novel sensor is constructed by an electrical cabinet and an optical reflecting and receiving end. The DFB laser is employed for targeting a strong H_2S line at 6 336.62 cm^(-1) in the fundamental absorption band of H_2S. The sensor performance, including the minimum detection limit and the stability, can be improved by reducing the laser intensity drift and common mode noise by means of the WMS-2f/1f technique. The experimental results indicate that the linearity and response time of the sensor are 0.999 26 and 6 s(in concentration range of 15.2—45.6 mg/m^3), respectively. The maximum relative deviation for continuous detection(60 min) of 30.4 mg/m^3 H_2S is 0.48% and the minimum detection limit obtained by Allan variance is 79 μg/m^3 with optimal integration time of 32 s. The optical H_2S sensor can be applied to environmental monitoring and industrial production, and it has significance for real-time online detection in many fields.

IP-geolocater:a more reliable IP geolocation algorithm based on router error training

Location based services(LBS)are widely utilized,and determining the location of users’IP is the foundation for LBS.Constrained by unstable delay and insufficient landmarks,the existing geolocation algorithms have problems such as low geolocation accuracy and uncertain geolocation error,difficult to meet the requirements of LBS for accuracy and reliability.A new IP geolocation algorithm based on router error training is proposed in this manuscript to improve the accuracy of geolocation results and obtain the current geolocation error range.Firstly,bootstrapping is utilized to divide the landmark data into training set and verification set,and/24 subnet distribution is utilized to extend the training set.Secondly,the path detection is performed on nodes in the three data sets respectively to extract the metropolitan area network(MAN)of the target city,and the geolocation result and error of each router in MAN are obtained by training the detection results.Finally,the MAN is utilized to get the target’s location.Based on China’s 24,254 IP geolocation experiments,the proposed algorithm has higher geolocation accuracy and lower median error than existing typical geolocation algorithms LBG,SLG,NNG and RNBG,and in most cases the difference is less than 10km between estimated error and actual error.