scholarly journals A Novel User Layer Cloud Security Model based on Chaotic Arnold Transformation using Fingerprint Biometric Traits

2021 ◽  
Vol 3 (1) ◽  
pp. 36-51
Author(s):  
Samuel Manoharan J
Keyword(s):  
Research Work ◽  
User Interaction ◽  
Recent Decade ◽  
Security And Privacy ◽  
Detection Accuracy ◽  
Security Model ◽  
Legitimate User ◽  
Cloud Models ◽  
Prime Concern ◽  
Processing And Storage

Cloud computing models have emerged to be a key player in the field of information processing in the recent decade. Almost all the services related to data processing and storage from firms work on a cloud platform providing the requested services to the consumers at any point of time and location. Security is an essential concern in cloud models as they primarily deal with data. Since multitude of user’s access cloud by way of storing confidential information in the virtual storage platform or accessing vital data from archives, security and privacy is of prime concern. This has been taken as the motivation of this research work. An effective Chaotic based Biometric authentication scheme for user interaction layer of cloud is proposed and implemented in this research paper. The proposed method uses fingerprint as the biometric trait and varies from conventional methods by utilizing a N-stage Arnold Transform to securely verify the claim of the so-called legitimate user. The experimentations have been compared with existing benchmark methods and superior performances observed in terms of detections, false detection accuracy etc.

scholarly journals Anomaly Detection Using Deep Neural Network for IoT Architecture

2021 ◽  
Vol 11 (15) ◽  
pp. 7050
Author(s):  
Zeeshan Ahmad ◽  
Adnan Shahid Khan ◽  
Kashif Nisar ◽  
Iram Haider ◽  
Rosilah Hassan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Traffic Monitoring ◽  
Detection Accuracy ◽  
Learning Models ◽  
Prime Concern ◽  
Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals Carpooling in Connected and Autonomous Vehicles: Current Solutions and Future Directions

2022 ◽  
Author(s):  
Farkhanda Zafar ◽  
Hasan Ali Khattak ◽  
Moayad Aloqaily ◽  
Rasheed Hussain
Keyword(s):  
Business Model ◽  
Autonomous Vehicles ◽  
Research Work ◽  
Relevant Information ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Survey ◽  
One Stop ◽  
Future Research Directions

Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.

scholarly journals MACHINE LEARNING APPROACH FOR CRYPTOSYSTEM SUGGEST IN EDUCARE OVER CLOUDS

2021 ◽  
Vol 9 (6) ◽  
pp. 98-103
Author(s):  
Ravish G K ◽  
Thippeswamy K
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Cloud Services ◽  
Learning System ◽  
Security And Privacy ◽  
Great Promise ◽  
Sensitive Information ◽  
Online Systems ◽  
Machine Learning Approach ◽  
Time Requirements

In the current situation of the pandemic, global organizations are turning to online functionality to ensure survival and sustainability. The future, even though uncertain, holds great promise for the education system being online. Cloud services for education are the center of this research work as they require security and privacy. The sensitive information about the users and the institutions need to be protected from all interested third parties. since the data delivery on any of the online systems is always time sensitive, the have to be fast. In previous works some of the algorithms were explored and statistical inference based decision was presented. In this work a machine learning system is designed to make that decision based on data type and time requirements.

scholarly journals WND-Identifier: Automated and Efficient Identification of Wireless Network Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fangzhou Zhu ◽  
Liang Liu ◽  
Simin Hu ◽  
Ting Lv ◽  
Renjun Ye
Keyword(s):  
User Interaction ◽  
Classification Model ◽  
Security And Privacy ◽  
Wireless Devices ◽  
Product Model ◽  
Widespread Application ◽  
Device Identification ◽  
Public Networks ◽  
Application Logic ◽  
Precision Device

The widespread application of wireless communication technology brings great convenience to people, but security and privacy problems also arise. To assess and guarantee the security of wireless networks and user devices, discovering and identifying wireless devices become a foremost task. Currently, effective device identification is still a challenging issue, as device fingerprinting requires huge training datasets and is difficult to expand, and rule-based identification is not accurate and reliable enough. In this paper, we propose WND-Identifier, a universal and extensible framework for the identification of wireless devices, which can generate high-precision device labels (vendor, type, and product model) efficiently without user interaction. We first introduce the concept of device-info-related network protocols. WND-Identifier makes full use of the natural language features in such protocol messages and combines with the device description in the welcome page, thereby utilizing extraction rules to generate concrete device labels. Considering that the device information in the protocol messages may be incomplete or forged, we further take advantage of the application logic independence and stability of the device-info-related protocol, so as to build a multiprotocol text classification model, which maps the device to a known label. We conduct experiments in homes and public networks and present three application scenarios to verify the effectiveness of WND-Identifier.

Influence of filler on erosion behavior of polymer composites: A comprehensive review

2018 ◽  
Vol 37 (15) ◽  
pp. 1011-1019 ◽  
Author(s):  
S Vigneshwaran ◽  
M Uthayakumar ◽  
V Arumugaprabu ◽  
R Deepak Joel Johnson
Keyword(s):  
Polymer Composites ◽  
Polymer Matrix ◽  
Erosion Resistance ◽  
Polymer Matrix Composites ◽  
Research Work ◽  
Recent Decade ◽  
Extensive Study ◽  
Filler Materials ◽  
Matrix Composites ◽  
Filled Composite

In recent decade, polymer matrix composites were extensively used in various engineering applications owing to their advanced properties over conventional materials and enhanced performance. This motivated the researchers to generate an extensive study and research work on polymer composites. In recent studies, the erosion properties of the polymer composite attract increasing attention among researchers. The potential enhancement in the erosion resistance property of filled composites tempted the researchers to find the feasibility of using various filler materials in polymer matrix for specific erosion resistance applications. However, only limited numbers of literature are available concerning the tribological performance of the filled composite. Hence in this study, an objective was set to review the various literature that explain the erosion characteristics of filled composites.

Security enabled UAVs for Tech-Agriculture monitoring rice crops using FIBOR architecture

2021 ◽  
pp. 119-126
Author(s):  
J V N Lakshmi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Continuous Monitoring ◽  
Security And Privacy ◽  
Security Model ◽  
Indian Agriculture ◽  
Collaborative Environment ◽  
Design Flexibility ◽  
Proposed Model ◽  
Farming Industry ◽  
Technical Collaboration

Unmanned Aerial Vehicles usage has significantly improved in all the sectors. Various industries are using drones as a platform for development with eco- nomic investment. Drastic advancement in design, flexibility, equipment and technical improvements has a great impact in creating airborne domain of IoT. Hence, drones have become a part of farming industry. Indian agriculture economy concentrates more on producing rice as this is considered as a staple food in various states. For increasing the production of rice sensors are equipped in the fields to track the water supply and humidity components. Whereas, identifying weeds, early stages of disease detection, recognizing failed crops, spraying fertilizers and continuous monitoring from bleats, locust and other dangerous insects are some of the technical collaboration with UAVs with respect farming sector. However, use of UAVs in real time environment involves many security and privacy challenges. In order to preserve UAVs from external vulnerabilities and hacking the collaborative environment requires a tough security model. In this proposed article a framework is implemented applying FIBOR security model on UAVs to suppress the threats from data hackers and protect the data in cloud from attackers. This proposed model enabled with drone technology provides a secured framework and also improves the crop yield by 15% by adapting a controlled network environment.

A Compliance-Driven Framework for Privacy and Security in Highly Regulated Socio-Technical Environments

2021 ◽  
pp. 933-962
Author(s):  
Ayda Saidane ◽  
Saleh Al-Sharieh
Keyword(s):  
Business Processes ◽  
System Development ◽  
Regulatory Compliance ◽  
Security And Privacy ◽  
Modeling Framework ◽  
Privacy And Security ◽  
Legitimate User ◽  
Simulation Based ◽  
Driven System ◽  
City System

Regulatory compliance is a top priority for organizations in highly regulated ecosystems. As most operations are automated, the compliance efforts focus on the information systems supporting the business processes of the organizations and, to a lesser extent, on the humans using, managing, and maintaining them. Yet, the human factor is an unpredictable and challenging component of a secure system development and should be considered throughout the development process as both a legitimate user and a threat. In this chapter, the authors propose COMPARCH as a compliance-driven system engineering framework for privacy and security in socio-technical systems. It consists of (1) a risk-based requirement management process, (2) a test-driven security and privacy modeling framework, and (3) a simulation-based validation approach. The satisfaction of the regulatory requirements is evaluated through the simulation traces analysis. The authors use as a running example an E-CITY system providing municipality services to local communities.

scholarly journals A Hyperconnected Smart City Framework

2020 ◽  
Vol 24 ◽  
Author(s):  
Naureen Naqvi ◽  
Sabih Ur Rehman ◽  
Zahidul Islam
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Security And Privacy ◽  
Directed Network ◽  
Web Based ◽  
Sustainable Environment ◽  
Processing And Storage ◽  
The Impact ◽  
And Storage

Recent technological advancements have given rise to the concept of hyper-connected smart cities being adopted around the world. These cities aspire to achieve better outcomes for citizens by improving the quality of service delivery, information sharing, and creating a sustainable environment. A smart city comprises of a network of interconnected devices also known as IoT (Internet of Things), which captures data and transmits it to a platform for analysis. This data covers a variety of information produced in large volumes also known as Big Data. From data capture to processing and storage, there are several stages where a breach in security and privacy could result in catastrophic impacts. Presently there is a gap in the centralization of knowledge to implement smart city services with a secure architecture. To bridge this gap, we present a framework that highlights challenges within the smart city applications and synthesizes the techniques feasible to solve them. Additionally, we analyze the impact of a potential breach on smart city applications and state-of-the-art architectures available. Furthermore, we identify the stakeholders who may have an interest in learning about the relationships between the significant aspects of a smart city. We demonstrate these relationships through force-directed network diagrams. They will help raise the awareness amongst the stakeholders for planning the development of a smart city. To complement our framework, we designed web-based interactive resources that are available from http://ausdigitech.com/smartcity/.

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