Improved Framework for Blockchain Application Using Lattice Based Key Agreement Protocol

One of the most recent challenges in communicationsystem and network system is the privacy and security ofinformation and communication session. Blockchain is one oftechnologies that use in sensing application in different importantenvironments such as healthcare. In healthcare the patient privacyshould be protected use high security system. Key agreementprotocol based on lattice ensure the authentication and highprotection against different types of attack especiallyimpersonation and man in the middle attack where the latticebased protocol is quantum-withstand protocol. Proposed improvedframework using lattice based key agreement protocol forapplication of block chain, with security analysis of manyliteratures that proposed different protocols has been presentedwith comparative study. The resultant new framework based onlattice overcome the latency limitation of block chain in the oldframework and lowered the computation cost that depend onElliptic curve Diffie-Hellman. Also, it ensures high privacy andprotection of patient’s informatio

Database techniques for resilient network monitoring and inspection

Network connection logs have long been recognized as integral to proper network security, maintenance, and performance management. This paper provides a development of distributed systems and write optimized databases: However, even a somewhat sizable network will generate large amounts of logs at very high rates. This paper explains why many storage methods are insufficient for providing real-time analysis on sizable datasets and examines database techniques attempt to address this challenge. We argue that sufficient methods include distributing storage, computation, and write optimized datastructures (WOD). Diventi, a project developed by Sandia National Laboratories, is here used to evaluate the potential of WODs to manage large datasets of network connection logs. It can ingest billions of connection logs at rates over 100,000 events per second while allowing most queries to complete in under one second. Storage and computation distribution are then evaluated using Elastic-search, an open-source distributed search and analytics engine. Then, to provide an example application of these databases, we develop a simple analytic which collects statistical information and classifies IP addresses based upon behavior. Finally, we examine the results of running the proposed analytic in real-time upon broconn (now Zeek) flow data collected by Diventi at IEEE/ACM Supercomputing 2019

Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification

This paper presents a novel model for arrhythmia detection based on a cascading technique that utilizes a combination of the One-Sided Selection (OSS) method, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, this model denoted by (OWSK) model to classify four types of electrocardiogram (ECG) heartbeats following inter-patient scheme. The OWSK model consists of three stages. The first stage involves resampling using the One-Sided Selection (OSS) method to solve the imbalance problem and reduce data by removing noisy, borderline, and redundant samples. The second stage involves using Wavelet Transformation (WT) and Power Spectral Density (PSD) to extract the most relevant frequency domain features. The third stage involves a cascading process by constructing the classifier from SVM trained on the whole dataset to classify normal and abnormal beats. Then, KNN (K-Nearest Neighbors) is trained on only the three irregular minority classes to classify the three types of arrhythmias for the detection of ventricular ectopic beats, supraventricular ectopic beats, and fusion beats (V, S, and F). The performance of the proposed model is evaluated in terms of different metrics, including accuracy, recall, precision, and F1 score. The results show the superiority of the proposed model in medical diagnosis compared to the latest works, where it achieves 90%, 90%, 93%, and 91% for accuracy, recall, precision, and F1 score under the inter-patient paradigm and 98%, 98%, 98%, and 98% under the intra-patient paradigm