A New Method IBE Interfaced with Private Key Generation and Public Key Infrastructure to Achieve High Data Security

A New Method IBE Interfaced with Private Key Generation and Public Key Infrastructure to Achieve High Data Securit

Detecting DDoS Attacks in Stub Domains

DoS attacks have least impact when mitigated close to the attacks' source. This is more important for Distributed DoS (DDoS) attacks since they are difficult to road Hudson, NH zipmitigate at the victim without affecting service to legitimate flows. This is a challenging task since DDoS attack traffic may have relatively low flow rates and attack packets are indistinguishable from legitimate packets. Current source-end detection schemes such as MULTOPS and D-WARD are centralized and hence, are not easily deployable in multi-gateway stub networks with asymmetric traffic. We present a scalable, distributed DDoS detection system that can be deployed in single- as well as multi-homed stub networks to detect DDoS attacks using TCP packets. The detection system can detect attacks with very low flow rates and in multi-gateway networks, even with significant asymmetric TCP flows. We evaluate the performance of our detection system using extensive packet level simulations under different attack scenarios. Our results show that with relatively less node state and processing, in networks with symmetric flows, our system can accurately detect attack flows that are one-third the intensity of an average flow in the network. In the case of multi-gateway networks, the detection system can detect all attacks for all rates of asymmetry when the attack rate is at least five times the average flow rate in the network. We extend the system to detect attacks aimed at multiple hosts in a subnet instead of a single host. Subnet attacks seem more diffused for detection schemes designed to detect host attacks. Hence, it is harder for these schemes to detect these attacks. Our subnet attack detection scheme can detect attacks that target hosts in large subnets (/21) and in the presence of non-attack traffic to other hosts in the subnet. Our packet level simulations show that, in single gateway networks, our scheme can detect attacks with an aggregate flow intensity equal to an average flow in the network in less than a minute. Using these simulations, we also show that our scheme detects attacks in networks with up to four gateways and when up to 50\% of the flows are asymmetric

Improving Performance and Flexibility of Fabric-Attached Memory Systems

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage of the overall memory capacity will likely be underutilized, especially when there are many jobs with small memory footprints. Thus, as HPC systems are moving towards the exascale era, better utilization of memory is strongly desired. Moreover, as new memory technologies come on the market, the flexibility of upgrading memory and system updates becomes a major concern since memory modules are tightly coupled with the computing nodes. To address these issues, vendors are exploring fabric-attached memories (FAM) systems. In this type of system, resources are decoupled and are maintained independently. Such a design has driven technology providers to develop new protocols, such as cache-coherent interconnects and memory semantic fabrics, to connect various discrete resources and help users leverage advances in-memory technologies to satisfy growing memory and storage demands. Using these new protocols, FAM can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. However, adopting FAM in HPC systems brings in new challenges. Since memory is disaggregated and is accessed through fabric networks, latency in accessing memory (efficiency) is a crucial concern. In addition, quality of service, security from neighbor nodes, coherency, and address translation overhead to access FAM are some of the problems that require rethinking for FAM systems. To this end, we study and discuss various challenges that need to be addressed in FAM systems. Firstly, we developed a simulating environment to mimic and analyze FAM systems. Further, we showcase our work in addressing the challenges to improve the performance and increase the feasibility of such systems; enforcing quality of service, providing page migration support, and enhancing security from malicious neighbor nodes

BREATH HOLDING TIME AND OXYGEN SATURATION IN COVID AFFECTED NURSING STUDENTS-A COMPARATIVE STUDY

Objective: The present study is based on a novel approach of validated breath-holding technique and efficiency of SpO2 in the adverse COVID-19 outcomes and comparison with normal subjects. Methods: It is a prospective observational study conducted in residential/private nursing colleges, St. Luke’s School and College of Nursing and Smt. Vijaya Luke’s College of Nursing, Visakhapatnam during the period July 2021. Fifty-three student nurses affected with mild COVID-19, 35 student nurses affected with moderate COVID-19, aged 18–23 years were enrolled after taking thorough history about COVID-19 that is after 2 months of complete recovery. They were classified based on the symptom history in which the subjects without symptoms or mild symptoms were taken as mildly affected, whereas subjects with severe symptoms with mild fluctuations in SpO2 who didn’t require hospitalization were classified as moderately affected. The study included 109 normal control cases who are never affected with COVID-19 viral infection. In all the subjects, the oxygen saturation was measured using pulse oxymeter and their Breath holding times were also measured using standard protocols. Results: The mean value of BHT was significantly reduced from normal 16.7339±3.4 to 12.8571±5.1 (p<0.05) in moderate cases. When oxygen saturation levels were compared before and after the breath holding in normal, mild and moderate cases the results were significant. However, when the oxygen saturation levels were compared between normal and mild COVID-19 cases the values were insignificant (p=0.4) and at the same time when the oxygen saturation levels were compared between normal and moderate COVID-19 cases the values were significant (p=0.0001). Conclusion: According to the findings, breath-holding does not need greater energy expenditure or cardiac output, and it eliminates walking and the related contamination of bystanders as occurring with pulse oximeter. Breath holding time is a determinant of respiratory capacity, when used as parameter helps in assessing the progression of lung injury, it gives an idea about respiratory fitness especially in this COVID era. Breath holding time and fluctuations in SpO2 when used conjointly we can assess degree of lung damage so that further treatment such as the continuity of medication, practicing of breathing exercises with or without medical treatment can be planned. This simple non-invasive tool can be used for the self-assessment of improvement in post-Covid patients. Future validation studies validate this hypothesis, measurement of these basic, innovative surrogates requires minimum inventory (i.e., a means to record oximetry and a timing device) and could feasibly provide a useful way to evaluate risks of future deterioration under under-resourced conditions

Fetal ECG extraction using wiener, SVD and ICA algorithms

Fetal Electrocardiogram (FECG) signal recording is one of the best techniques for Heart signal monitoring of fetus. It is also used to monitor health condition of fetus in pregnancy period continuously. Fetal electrocardiogram is nothing but wave form which shows electrical activity of fetus’s heart. FECG is extracted from a signal recorded on the mother’s abdomen, which is an indirect method (non-invasive method). Abdomen signal includes mother electrocardiogram (MECG) signal, FECG signal and noise signal. Different indirect methods to extract the Fetal Electrocardiogram (FECG) signal from an ECG recorded on the mother’s abdomen have been proposed. In this thesis, three methods are used, which are as follows: Singular Value Decomposition (SVD) method, Independent Component Analysis (ICA) method, and Weiner Filtering method. Wiener filter uses the linear least square estimation; SVD uses the variance as measure which is similar to Eigen value decomposition and ICA uses the fourth order moment, kurtosis. SVD and ICA are comes under statistical domain and also blind source separation, whereas Wiener filter comes under Fourier domain. The mentioned methods use signal processing techniques for extracting FECG from Abdominal Electrocardiogram (AECG) and uses a multi-channel data/signal. The advantages and disadvantages of each method are discussed. The methods have applied on synthetic ECG signals of 10 seconds with a sampling rate of 256Hz. Efficiencies of all the methods are compared together based on the few important criterions, which are output waveform, PSD, and SNR. The results are stated and best method based on the criterions is selected

Security Implementation Using Present-Puffin Protocol

The Electronic Product Code Generation 2 (EPC GEN2) protocol does not have any technique to authenticate the Reader before it gives access to the Tag’s memory. In this paper, we use security implementation and mutual authentication between tag and reader of three different lightweight ciphers. We used Hummingbird (HB), PRESENT, and Extended Tiny Encryption Algorithm (XTEA) to encrypt the data and implemented all three algorithms to FPGA devices. We finally implemented PRESENT with PUFFIN as a trail and we got better results compared to the former three ciphers based on performance, data blocks and execution time

Image Interpretation-Guided Supervised Classification Using Nested Segmentation

We present a new binary (two-class) supervised non-parametric classification approach that is based on iterative partitioning of multidimensional feature space into variably-sized and nested hyper-cubes (partitions). The proposed method contains elements of active learning and includes classifier to analyst queries. The spectral transition zone between two thematic classes (i.e., where training labels of different classes overlap in feature space) is targeted through iterative training derivation. Three partition categories are defined: pure, indivisible and unlabeled. Pure partitions contain training labels from only one class, indivisible partitions contain training data from different classes, and unlabeled partitions do not contain training data. A minimum spectral tolerance threshold defines the smallest partition volume to avoid over-fitting. In this way the transition zones between class distributions are minimized, thereby maximizing both the spectral volume of pure partitions in the feature space and the number of pure pixels in the classified image. The classification results are displayed to show each classified pixel\u27s partition category (pure, unlabeled and indivisible). Mapping pixels belonging to unlabeled partitions serves as a query from the classifier to the analyst, targeting spectral regions absent of training data. The classification process is repeated until significant improvement of the classification is no longer realized or when no classification errors and unlabeled pixels are left. Variably-sized partitions lead to intensive training data derivation in the spectral transition zones between the target classes. The methodology is demonstrated for surface water and permanent snow and ice classifications using 30 m conterminous United States Landsat 7 Enhanced Thematic Mapper Plus (ETM +) data time series from 2006 to 2010. The surface water result was compared with Shuttle Radar Topography Mission (SRTM) water body and National Land Cover Database (NLCD) open water classes with an overall agreement greater than 99% and Kappa coefficient greater than 0.9 in both of cases. In addition, the surface water result was compared with a classification generated using the same input data and a standard bagged Classification and Regression Tree (CART) classifier. The nested segmentation and CART-generated products had an overall agreement of 99.9 and Kappa coefficient of 0.99

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology