A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Ensemble Machine Learning Model

One of the most  serious threat to network security is Denial of service (DOS) attacks. Internet and computer networks are now important parts of our businesses and daily lives. Malicious actions have become more common as our reliance on computers and communication networks has grown. Network threats are a big problem in the way people communicate today. To make sure that the networks work well and that users' information is safe, the network data must be watched and analysed to find malicious activities and attacks. Flooding may be the simplest DDoS assault. Computer networks and services are vulnerable to DoS and DDoS attacks. These assaults flood target systems with malicious traffic, making them unreachable to genuine users. The work aims to enhance the resilience of network infrastructures against these attacks and ensure uninterrupted service delivery. This research develops and evaluates enhanced DoS/DDoS detection methods. DoS attacks usually stop or slow down legal computer or network use. Denial-of-service (DoS) attacks prevent genuine users from accessing and using information systems and resources. The OSI model's layers make up the computer network. Different types of DDoS strikes target different layers. The Network Layer can be broken by using ICMP Floods or Smurf Attacks. The Transport layer can be attacked using UDP Floods, TCP Connection Exhaustion, and SYN Floods. HTTP-encrypted attacks can be used to get through to the application layer. DoS/DDoS attacks are malicious attacks. Protect network data from harm. Computer network services are increasingly threatened by DoS/DDoS attacks. Machine learning may detect prior DoS/DDoS attacks. DoS/DDoS attacks proliferate online and via social media. Network security is IT's top priority. DoS and DDoS assaults include ICMP, UDP, and the more prevalent TCP flood attacks. These strikes must be identified and stopped immediately. In this work, a stacking ensemble method is suggested for detecting DoS/DDoS attacks so that our networked data doesn't get any worse. This paper used a method called "Ensemble of classifiers," in which each class uses a different way to learn. In proposed  methodology Experiment#1 , I used the Home Wifi Network Traffic Collected and generated own Dataset named it as MywifiNetwork.csv, whereas in proposed methodology Experiment#2, I used the kaggle repository “NSL-KDD benchmark dataset” to perform experiments in order to find detection accuracy of dos attack detection using python language in jupyter notebook. The system detects attack-type or legitimate-type of network traffic during detection ML classification methods are used to compare how well the suggested system works. The results show that when the ensembled stacking learning model is used, 99% of the time it is able to find the problem. In proposed methodology two Experiments are implemented for comparing detection accuracy with the existing techniques. Compared to other measuring methods, we get a big step forward in finding attacks. So, our model gives a lot of faith in securing these networks. This paper will analyse the behaviour of network traffics

Jellyfish as an export commodity

Recently, jellyfish blooms have been reported with increased frequency from several parts of the world and it has been suggested that this phenomenon might be related to over-fishing and other human activities that are driving marine ecosystems off balance. It has contributed to the formulation of the “fishing down the food chain” hypothesis, which is based on the assumption that the reduction in large species marine predator populations is promoting the growth of organisms from lower levels of the food chain. Rising sea temperature is also considered as a reason for the occurrence of jellyfish fishery

Temperature Dependent Cyclic Deformation Mechanisms in Haynes 188 Superalloy

The cyclic deformation behavior of a wrought cobalt-base superalloy, Haynes 188, has been investigated over a range of temperatures between 25 and 1000 C under isothermal and in-phase thermomechanical fatigue (TMF) conditions. Constant mechanical strain rates (epsilon-dot) of 10(exp -3)/s and 10(exp -4)/s were examined with a fully reversed strain range of 0.8%. Particular attention was given to the effects of dynamic strain aging (DSA) on the stress-strain response and low cycle fatigue life. A correlation between cyclic deformation behavior and microstructural substructure was made through detailed transmission electron microscopy. Although DSA was found to occur over a wide temperature range between approximately 300 and 750 C the microstructural characteristics and the deformation mechanisms responsible for DSA varied considerably and were dependent upon temperature. In general, the operation of DSA processes led to a maximum of the cyclic stress amplitude at 650 C and was accompanied by pronounced planar slip, relatively high dislocation density, and the generation of stacking faults. DSA was evidenced through a combination of phenomena, including serrated yielding, an inverse dependence of the maximum cyclic hardening with epsilon-dot, and an instantaneous inverse epsilon-dot sensitivity verified by specialized epsilon-dot -change tests. The TMF cyclic hardening behavior of the alloy appeared to be dictated by the substructural changes occuring at the maximum temperature in the TMF cycle

Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods

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Not AvailableGlobally, maize is an important cereal food crop with the highest production and productivity. Among the biotic constraints that limit the productivity of maize, the recent invasion of fall armyworm (FAW) in India is a concern. The first line of strategy available for FAW management is to evaluate and exploit resistant genotypes for inclusion in an IPM schedule. Screening for resistant maize genotypes against FAW is in its infancy in India, considering its recent occurrence in the country. The present work attempts to optimize screening techniques suited to Indian conditions, which involve the description of leaf damage rating (LDR) by comparing injury levels among maize genotypes and to validate the result obtained from the optimized screening technique by identification of lines potentially resistant to FAW under artificial infestation. Exposure to 20 neonate FAW larvae at the V 5 phenological stage coupled with the adoption of LDR on a 1–9 scale aided in preliminary characterize maize rize maize genotypes as potentially resistant, moderately resistant, and susceptible. The LDR varies with genotype, neonate counts, and days after infestation. The genotypes, viz., DMRE 63, DML-163-1, CML 71, CML 141, CML 337, CML 346, and wild ancestor Zea mays ssp. parviglumis recorded lower LDR ratings against FAW and can be exploited for resistance breeding in maize.ICAR-NAS

Performance Analysis on FZPL Antenna‟s to Enhance Spatial Resolution, Focusing Efficiency.

efficiency play very important role for getting good results. There is dependency between resolution and FZP diameter. So I described analytical estimations on radius, diameter etc. This paper presents the dependency of focusing efficiency on (1 st order diffraction wave) on number of zone plates, dependency of focusing efficiency on interlayer distance(l) between zone plates, dependency of (r n) on number of zone plates, dependency of (r n) on focal length(f), estimated values of ‘Z ’ and ‘l’. It also presents the spatial resolution improvement with the help of pinhole diffraction holography with the help of analytical estimations and graphs, also it describes the analytical estimations on radial distance to the different bright zones(r), width of the zone(w), interlayer distance between zone plates(l). Keywords-- pinhole diffraction holography, multilayer zone plates, interlayer distance, focusing efficiency, stacked multilevel Fresnel zone plate, FZPL antennas, FZPA, Fresnel-zone geometry, Spatial resolution. I

Provenance studies of Chirala coastal glass sand deposit, east coast of India

195-197Mineralogy and chemical and optical properties of Chirala coastal glass sand deposit have been studied. Common hornblends and epidotes are predominant among nonopaque minerals. Euhedral zircons are common. Hornblende and epidote abundance and their chemical and optical properties suggest that Nellore schist belt as the chief source for these sands. Shape and length-breadth ratios of zircons further support the schist belt provenance. Low presence of garnets, sillimanites, rounded zircons and zircon high-elongation frequencies indicate minor contribution from khondalites and charnockites. Major confinement of Gundlkamma river to Dharwarian schistose rocks and Archaean granitic gneisses suggest that the Nellore schist belt might have been actively eroded and contributed the sediment to a large extent to this coastal sand belt

Grain Size Characteristics and Chemical Composition of Coastal Sands of Chirala, East Coast of India

136-138The moment and percentile measures indicate the depositional conditions in near shore marine environment for the Chirala white sands. Based on gradational changes in sands and linear nature of the belt extending parallel to the coast, the sand belt is identified to be a sand bar. The growth of the sand bar running parallel to the ancient coast line is related to the action of longshore currents. Grain size distribution and chemical constituents have indicated that the bulk sands and beneficiated sands are useful in foundry jobs and production of green bottle glass respectively

Laminar flow heat transfer in concentric equilateral triangular annular channels

614-622The problem of laminar flow heat transfer in channels of annular cross section formed by concentric equilateral triangles is studied. Numerical solutions for friction factor, and magnitude of minimum limiting Nusselt numbers for both constant heat input per unit length and constant wall temperature are obtained. The results obtained by the numerical method are compared with the available solutions for the limiting geometries of equilateral triangle and parallel plates. Experimental data for isothermal pressure drop and constant wall temperature boundary condition at the outer wall are presented. Test sections with length to equivalent diameter ratios of 13.85, 15.12 and 20.8 are employed in the present investigation. The Prandtl numbers are varied from 4 to 65 applying Glycerol-water mixture as coolant. Empirical correlations for isothermal friction factor and Nusselt number for constant wall temperature boundary conditions are presented