Publications
Decentralized security and data integrity of blockchain using deep learning techniques
Sep 3, 2020Journal Periodicals of Engineering and Natural Sciences
DOI 10.21533/pen
Issue 3
Volume 8
Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment. With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well. With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series. In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security. In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains. We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification. We evaluate LSTM based networks. As data for training we use historical security data in one-minute intervals from August 2019 to August 2020. We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model’s predictions. We analyze that performance and compare it with the buy and hold strategy. The simulation is carried out on bullish, bearish and stagnating time periods. In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task. We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better. The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy
Low energy consumption in manet network
Jun 2, 2020Journal Periodicals of Engineering and Natural Sciences
DOI 10.21533/pen
Issue 2
Volume 8
The aim of this paper is design and develop energy efficient MANET network in wireless networks. One of the most significant and effective protocol based on low energy consumption and number of Ad-hoc is MANET as remote directing convention source nodes forward in network simulator. Less number of nodes in the network would give low energy usage or consumption as the nodes in the network exceeds or increases that will also increase the energy consumption in the network. The designed MANET system is tried with 9, 12, 15 and 18 number of nodes in a system using network simulation-2 (NS-2). Henceforth source node needs to restart over and over which brings about low energy consumption use and use, ectiveness is less and packet space is additionally less and throughput is likewise less and more start to finish delay. Arrangement of this issue in MANET convention which is advanced as the node doesn't advance when demand arrived at their first it checked there is low energy consumption (battery lifetime) and until the node energy consumption is more noteworthy than the limit. Designed MANET examinations of the energy consumption and node energy consumption by maintaining a strategic distance from the low number of nodes in a network. By contrasting energy consumption and node it demonstrates that MANET is far superior to existing framework 802.11 protocol convention based on battery lifetime, energy consumption, throughput, and power transmission. We have performed a comparison between EEM and AODV routing protocol considering different measuring parameters.
In the Way of having an Optimum Wireless Ad-Hoc Sensor Networks: Analysis of Deploying Homogeneous and Inhomogeneous Nodes(
Dec 1, 2019Journal ARPN Journal of Engineering and Applied Sciences
Publisher Asian Research Publishing Network
DOI 10.21533/pen
Issue 14
Volume Special 8
The emerging technology of ad-hoc sensor networks helped to increase researches in this field due to a large number of applications that uses wireless ad hoc networks such as monitoring of the environment, intelligent agriculture, structure health, earthquake prediction, industrial control and target detection in military applications. Various analyses have been suggested for optimality of ad hoc networks; in our study for a given number of nodes we use a comparative analysis by using two kinds of sensors network, the first network with different type of sensors having different connectivity range and sensing coverage and the other network with same capabilities for sensors in terms of connectivity and coverage range and the aim is to have a clear view about the utility of deploying homogeneousor inhomogeneous wireless ad hoc sensor network. Analysis of sensors deployment with a homogeneous transmission range reveals better network connectivity. Therefore, deploying sensor nodes with different transmission range does not improve the connectivity of the network when a power constraint present. In addition, using inhomogeneous nodes does not help to reduce power consumption to maintain network availability. © 2019 Medwell Journals. All Rights Reserved.