K-DDoS-SDN: A distributed DDoS attacks detection approach for protecting SDN environment

Software-defined networking (SDN) is an advanced networking paradigm that decouples forwarding control logic from the data plane. Therefore, it provides a loosely-coupled architecture between the control and data plane. This separation provides flexibility in the SDN environment for addressing any transformations. Further, it delivers a centralized way of managing networks due to control logic embedded in the SDN controller. However, this advanced networking paradigm has been facing several security issues, such as topology spoofing, exhausting bandwidth, flow table updating, and distributed denial of service (DDoS) attacks. A DDoS attack is one of the most powerful menaces to the SDN environment. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we propose a Kafka-based distributed DDoS attacks detection approach for protecting the SDN environment named K-DDoS-SDN. The K-DDoS-SDN consists of two modules: (i) Network traffic classification (NTClassification) module and (ii) Network traffic storage (NTStorage) module. The NTClassification module is the detection approach designed using scalable H2O ML techniques in a distributed manner and deployed an efficient model on the two-nodes Kafka Streams cluster to classify incoming network traces in real-time. The NTStorage module collects raw packets, network flows, and 21 essential attributes and then systematically stores them in the HDFS to re-train existing models. The proposed K-DDoS-SDN designed and evaluated using the recent and publically available CICDDoS2019 dataset. The average classification accuracy of the proposed distributed K-DDoS-SDN for classifying network traces into legitimate and one of the most popular attacks, such as DDoS_UDP is 99.22%. Further, the outcomes demonstrate that proposed distributed K-DDoS-SDN classifies traffic traces into five categories with at least 81% classification accuracy.     

Novel analysis of the size and orientation dependent resonance phenomenon of the microstrip line coupled complementary split‐ring resonator

The purpose of this article is to provide a comprehensive investigation on the resonance phenomenon of microstrip line coupled complementary split‐ring resonator (CSRR) with different orientation and relative size. It is shown that when the relative size of the CSRR is smaller than the host line, the CSRR with its slit oriented orthogonal to the line axis will not excite effectively and show weak resonance behavior. However, when the slit is positioned along the line axis, the cross‐polarization effect comes into play, which excites the CSRR through the mixed coupling. To ensure the correctness, several numerical simulations are carried out for different substrate height and relative permittivity. Finally, a prototype is fabricated and measured for the experimental validation.     

Impacts of reservoirs on groundwater and vegetation: A study based on remote sensing and GIS techniques

Suspended sediment is an important parameter for the monitoring of water quality, water movement, erosion, and deposition. Quantitative suspended sediment concentrations have been identified through the analysis of remotely sensed airborne multi-spectral video imagery. From a statistical analysis of the data, a single band ( 675-725 nm) was shown to be sufficient for determining suspended sediment concentrations within lakes of the Mackenzie Delta, N.W.T., Canada. A statistical analysis and derived linear regression equation were used to quantitatively determine suspended sediment concentrations for a number of lakes within the Mackenzie Delta. The results were compared with a qualitative interpretation of simultaneous colour photography. The multi-spectral video imagery proved to have superior suspended sediment concentration resolution characteristics.     

GIS based surface hydrological modelling in identification of groundwater recharge zones

Digital elevation model (DEM) is a storehouse of a variety of hydrological information along with terrain characteristics. In recent years, automatic extraction of drainage network from DEM with the help of Geographical Information System (GIS) has become possible and is now being practised the world over for hydrological studies. In the present study, a comparative analysis of the drainage network derived from DEM and drainage extracted from surveyed topographical maps has been carried out. A comparative analysis based on nearest neighbour analysis on an intersection theme of two drainage networks showed that there is clustering (randomness<1) existing at places which show potential groundwater recharge zones. The suitable groundwater recharge zones identified in the drainage comparative analysis also show good correlation with the suitable recharge maps derived from remote sensing and GIS based procedure. In this study, two different watersheds (a) Dwarkeshwar in Bankura district, West Bengal, India, and (b) Kethan in Vidisha districts of Madhya Pradesh, India have been taken to analyse for identification of suitable groundwater recharge zones. The drainage comparative analysis approach developed and tested successfully in the present study is quick and reliable for the identification of suitable groundwater recharge zones particularly in a hard rock terrain.     

Accuracy assessment of MODIS,NOAA and IRS data in snow cover mapping under Himalayan conditions

Snow cover information is an essential parameter for a wide variety of scientific studies and management applications, especially in snowmelt runoff modelling. Until now NOAA and IRS data were widely and effectively used for snow‐covered area (SCA) estimation in several Himalayan basins. The suit of snow cover products produced from MODIS data had not previously been used in SCA estimation and snowmelt runoff modelling in any Himalayan basin. The present study was conducted with the aim of assessing the accuracy of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. The total SCA was estimated using these three datasets for 15 dates spread over 4 years. The results were compared with ground‐based estimation of snow cover. A good agreement was observed between satellite‐based estimation and ground‐based estimation. The influence of aspect in SCA estimation was analysed for the three satellite datasets and it was observed that MODIS produced better results. Snow mapping accuracy with respect to elevation was tested and it was observed that at higher elevation MODIS sensed more snow and proved better at mapping snow under mountain shadow conditions. At lower elevation, IRS proved better in mapping patchy snow cover due to higher spatial resolution. The temporal resolution of MODIS and NOAA data is better than IRS data, which means that the chances of getting cloud‐free scenes is higher. In addition, MODIS has an automated snow‐mapping algorithm, which reduces the time and errors incorporated during processing satellite data manually. Considering all these factors, it was concluded that MODIS data could be effectively used for SCA estimation under Himalayan conditions, which is a vital parameter for snowmelt runoff estimation.     

Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.

Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory

The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.     

Magnetic field-assisted photochemical machining (MFAPCM) of SS316L

In this paper, a new technique of photochemical machining (PCM) process has been described. The objective of this study is to investigate the effect of applying a magnetic field on the PCM of AISI 316?L stainless steel. The experiments were planned and conducted using a Full Factorial Design (FFD) approach. The control parameters selected were magnetic field, temperature, concentration and time. The analysis of the results shows significant improvement in the etch rate due to the application of a magnetic field. The highest etch rate was achieved at a concentration of 700?g/l, at the temperature of 60°C in a static magnetic field. The improvement of the etch rate is 2.5 times with the application of magnetic field as compared to the conventional PCM process.