An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Analysis of the Pencil of Conics with Double Complex Contact and Its Application to Camera Calibration

In this paper,we introduce a novel class of coplanar conies,the pencil of which can doubly contact to calibrate camera and estimate pose.We first analyze the properties of con-axes and con-eccentricity ellipses, which consist of a natural extending pattern of concentric circles.Then the general case that two ellipses have two repeated complex intersection points is presented.This degenerate configuration results in a one-parameter family of homographies which map the planar pattern to its image.Although it is unable to compute the complete homography,an indirect 3-degree polynomial or 5-degree polynomial constraint on intrinsic parameters from one image can also be used for camera calibration and pose estimation under the minimal conditions.Furthermore, this nonlinear problem can be treated as a polynomial optimization problem(POP) and the global optimization solution can be also obtained by using SparsePOP(a sparse semidefinite programming relaxation of POPs). Finally,the experiments with simulated data and real images are shown to verify the correctness and robustness of the proposed technique.

Vegetation dynamics in response to human and climatic factors in the Tanzanian Coast

This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes.The Tanzanian coast,rich in biodiversity,is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited.This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectro-radiometer(MODIS),Normalized Difference Vegetation Index(NDVI),meteorological,and Globeland30 Landsat data sets.Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000-2018 using the Mann-Kendall test and Pearson correlation respectively.The results reveal a significant positive trend in temperature(/?>0,Z=2.87)and a non-significant trend in precipitation(|Z|<1.96).A positive relationship between NDVI and precipitation is observed.Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large.Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land.The analysis of land use and land cover in the region reveals an increase in cultivated land,shrubland,grassland,built-up land and bare land,while forests,wetland and water all decreased between 2000 and 2020.The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products(firewood,timber,charcoal).The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources.