A Systematic Approach for a Secure Authentication System

Authentication is a process of verifying the credibility of a user who is trying to access classified or confidential information. There is a vast unfold in the number of internet users, and the demand for IoT devices, cloud services has been increasing; it is now essential more than ever to protect the data hosted on the internet. So, the authentication process cannot be relied on single-factor static authentication methods to verify the user credentials. All devices in the market are not equipped with biometric systems, so a form of multi-factor authentication which is independent of biometrics needs to be adopted for a secure authentication system. This paper portraits a systematic architecture to verify user credentials using specific parameters, trying to unfold patterns using machine learning algorithms based on user's past login records, thus trying to provide a safer and secure authentication process for the users.

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A Cancelable Iris- and Steganography-Based User Authentication System for the Internet of Things

Sensors ◽

10.3390/s19132985 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2985 ◽

Cited By ~ 4

Author(s):

Wencheng Yang ◽

Song Wang ◽

Jiankun Hu ◽

Ahmed Ibrahim ◽

Guanglou Zheng ◽

...

Keyword(s):

Internet Of Things ◽

User Authentication ◽

System Security ◽

Random Projection ◽

Biometric Data ◽

Iot Security ◽

Biometric Systems ◽

Authentication System ◽

Iot Devices ◽

The Internet Of Things

Remote user authentication for Internet of Things (IoT) devices is critical to IoT security, as it helps prevent unauthorized access to IoT networks. Biometrics is an appealing authentication technique due to its advantages over traditional password-based authentication. However, the protection of biometric data itself is also important, as original biometric data cannot be replaced or reissued if compromised. In this paper, we propose a cancelable iris- and steganography-based user authentication system to provide user authentication and secure the original iris data. Most of the existing cancelable iris biometric systems need a user-specific key to guide feature transformation, e.g., permutation or random projection, which is also known as key-dependent transformation. One issue associated with key-dependent transformations is that if the user-specific key is compromised, some useful information can be leaked and exploited by adversaries to restore the original iris feature data. To mitigate this risk, the proposed scheme enhances system security by integrating an effective information-hiding technique—steganography. By concealing the user-specific key, the threat of key exposure-related attacks, e.g., attacks via record multiplicity, can be defused, thus heightening the overall system security and complementing the protection offered by cancelable biometric techniques.

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AN EFFICIENT APPROACH TO IMPLEMENT FEDERATED CLOUDS

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19543 ◽

2017 ◽

Vol 10 (13) ◽

pp. 40

Author(s):

Pappu Sowmya ◽

R Kumar

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Low Cost ◽

Cloud Service ◽

Cloud Services ◽

The Internet ◽

Security Model ◽

Privacy And Security ◽

Security Issues ◽

Internet Users

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet, while providing the privacy and security. By using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, ‘virtualization’ is one of the techniques dividing memory into different blocks. In most of the existing systems there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: 1) allotting the VSM(VM Security Monitor) model for each virtual machine; 2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model.

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IoT Network Security: Threats, Risks, and a Data-Driven Defense Framework

IoT ◽

10.3390/iot1020016 ◽

2020 ◽

Vol 1 (2) ◽

pp. 259-285 ◽

Cited By ~ 1

Author(s):

Charles Wheelus ◽

Xingquan Zhu

Keyword(s):

Machine Learning ◽

Predictive Models ◽

Data Science ◽

Physical World ◽

Machine Learning Algorithms ◽

The Internet ◽

Security Threats ◽

Criminal Organizations ◽

Iot Devices ◽

Using Data

The recent surge in Internet of Things (IoT) deployment has increased the pace of integration and extended the reach of the Internet from computers, tablets and phones to a myriad of devices in our physical world. Driven by the IoT, with each passing day, the Internet becomes more integrated with everyday life. While IoT devices provide endless new capabilities and make life more convenient, they also vastly increase the opportunity for nefarious individuals, criminal organizations and even state actors to spy on, and interfere with, unsuspecting users of IoT systems. As this looming crisis continues to grow, calls for data science approaches to address these problems have increased, and current research shows that predictive models trained with machine learning algorithms hold great potential to mitigate some of these issues. In this paper, we first carry out an analytics approach to review security risks associated with IoT systems, and then propose a machine learning-based solution to characterize and detect IoT attacks. We use a real-world IoT system with secured gate access as a platform, and introduce the IoT system in detail, including features to capture security threats/attacks to the system. By using data collected from a nine month period as our testbed, we evaluate the efficacy of predictive models trained by means of machine learning, and propose design principles and a loose framework for implementing secure IoT systems.

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AN AUTHENTICATED, SECURE VIRTUALIZATION MANAGEMENT SYSTEM IN CLOUD COMPUTING

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19544 ◽

2017 ◽

Vol 10 (13) ◽

pp. 45

Author(s):

K Dinesh Kumar ◽

E Umamaheswari

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Low Cost ◽

Cloud Service ◽

Cloud Services ◽

The Internet ◽

Security Model ◽

Privacy And Security ◽

Security Issues ◽

Internet Users

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet while providing the privacy and security. Using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open-source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, “virtualization” is one of the techniques dividing memory into different blocks. In most of the existing systems, there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: (1) allotting the VM security monitor model for each virtual machine and (2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model.

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Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic

Electronics ◽

10.3390/electronics10232919 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2919

Author(s):

Rami J. Alzahrani ◽

Ahmed Alzahrani

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

New Technology ◽

Security Analysis ◽

Computation Time ◽

Machine Learning Algorithms ◽

The Internet ◽

Ddos Attacks ◽

Iot Devices

The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented.

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IMPLEMENTATION OF IOT TECHNOLOGY FOR DATA MONITORING VIA CLOUD SERVICES

T-Comm ◽

10.36724/2072-8735-2021-15-1-46-53 ◽

2021 ◽

Vol 15 (2) ◽

pp. 46-53

Author(s):

Veronika M. Antonova ◽

◽

Elena E. Malikova ◽

Alexey E. Panov ◽

Igor V. Spichek ◽

...

Keyword(s):

Undergraduate Students ◽

Research Work ◽

Cloud Service ◽

Access Point ◽

Automatic Monitoring ◽

Cloud Services ◽

The Internet ◽

Long Term Storage ◽

Iot Devices

An operating device has been designed for long term aggregating, storing and visualizing climate records with a view to their further publication in a cloud service. In order to address the given problem a number of technical issues related to the device concept development and its operating algorithms were solved. The device runs using the MQTT protocol and a microcontroller unit based on ESP8266 chip which is designated for the application in Internet of Things (IoT) devices. The designed system is based on open-source software and allows providing access to the received data to all authorized users. The system is expanded easily since the number of attached sensors and peripheral units can change and the program can be transformed so as to solve emerging tasks. The ability to connect the Internet from any access point provides the mobility for the device and permits to make measurements within the range of a Wi-Fi network. In some instances, it is convenient to use smart phones or tablets that have access to the Internet via cellular networks for research and scientific experiments. In this case, mobile devices can act as monitors to control the system operation. This device can be useful for carrying out research work when data collection over a long period of time and long-term storage of information with the possibility of its further processing are essential. The examples are automatic monitoring of the equipment, medical supervision of patients’ health or gathering and processing of various climate parameters. Undergraduate students can also make use of the developed device when studying IoT technology.

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Edge Machine Learning for AI-Enabled IoT Devices: A Review

Sensors ◽

10.3390/s20092533 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2533 ◽

Cited By ~ 6

Author(s):

Massimo Merenda ◽

Carlo Porcaro ◽

Demetrio Iero

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Machine Learning Algorithms ◽

The Internet ◽

Learning Models ◽

Iot Devices ◽

High Level ◽

And Behavior ◽

The Internet Of Things ◽

Machine Learning Models

In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.

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Design of Low Power Artificial Intelligence Model for Resilience of IoT Devices

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c1124.1083s19 ◽

2019 ◽

Vol 8 (3S) ◽

pp. 620-624

Keyword(s):

Data Transmission ◽

Personal Data ◽

Sensor Data ◽

The Internet ◽

Market Place ◽

Research Focus ◽

Precise Data ◽

Internet Users ◽

Medical Health ◽

Iot Devices

Internet users are keep on increasing every day and may reach billions and billions of users in the next year, the users of android mobiles for connecting their utilities with internet making them saving their time and allow fast and precise data transmission. Most of the communication, entertainment, medical, health, life and educational activities are showcased into the internet to increase the market place better. The increase in android users may also increase the lack of security on our personal data which is saved in the cloud. The study is focused on most of the devices connected with IoT and their decision making capability on sensible things like real word sensor data or malwares etc. The methodologies user in the industry to safeguard the data, the techniques involved in the detection of malwares etc. The study also motivate us to find out the extendable research focus on Resilient management in IoT devices during malware detection.

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Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i4.pp766-771 ◽

2020 ◽

Vol 9 (4) ◽

pp. 766

Author(s):

Fardin Abdali-Mohammadi ◽

Maytham N. Meqdad ◽

Seifedine Kadry

Keyword(s):

Internet Of Things ◽

Wearable Sensors ◽

Fuzzy Model ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

The Internet ◽

Diagnosis System ◽

System A ◽

Iot Devices ◽

To Receive

Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.

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The Effect of Suicide-Related Internet Use on Users’ Mental Health

Crisis ◽

10.1027/0227-5910/a000201 ◽

2013 ◽

Vol 34 (5) ◽

pp. 348-353 ◽

Cited By ~ 25

Author(s):

Hajime Sueki

Keyword(s):

Mental Health ◽

Young Adults ◽

Suicidal Ideation ◽

Internet Use ◽

Panel Study ◽

Psychological Effects ◽

The Internet ◽

Negative Effects ◽

Suicide Methods ◽

Internet Users

Background: Previous studies have shown that suicide-related Internet use can have both negative and positive psychological effects. Aims: This study examined the effect of suicide-related Internet use on users’ suicidal ideation, depression/anxiety tendency, and loneliness. Method: A two-wave panel study of 850 Internet users was conducted via the Internet. Results: Suicide-related Internet use (e.g., browsing websites about suicide methods) had negative effects on suicidal ideation and depression/anxiety tendency. No forms of suicide-related Internet use, even those that would generally be considered positive, were found to decrease users’ suicidal ideation. In addition, our results suggest that the greater the suicidal ideation and feelings of depression and loneliness of Internet users, the more they used the Internet. Conclusion: Since suicide-related Internet use can adversely influence the mental health of young adults, it is necessary to take measures to reduce their exposure to such information.

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