scholarly journals Cybersecurity for next generation healthcare in Qatar

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Mohammad Zubair ◽  
Devrim Unal ◽  
Abdulla Al-Ali ◽  
Thomas Reimann ◽  
Guillaume Alinier
Keyword(s):  
Deep Learning ◽  
Management System ◽  
Secure Communication ◽  
Identity Management ◽  
Detection System ◽  
Denial Of Service ◽  
Learning System ◽  
Dos Attacks ◽  
Privacy And Security ◽  
Identity Management System

Background: IoMT (Internet of Medical Things) devices (often referred to IoMT domain) have the potential to quickly diagnose and monitor patients outside the hospital by transmitting information through the cloud domain using wireless communication to remotely located medical professionals (user domain). shows the proposed IoMT framework designed to improve the privacy and security of the healthcare infrastructure. Methods: The framework consists of four modules: 1. Intrusion Detection System (IDS) using deep learning (DL) to identify bluetooth-based Denial-of-Service (DoS)-attacks on IoMT devices and is deployed on edge-computing to secure communication between IoMT and edge. 2. IDS is backed up with identity-based cryptography to encrypt the data and communication path. 3. Besides the identity-management system (to authenticate users), it is modeled with aliveness detection using face authentication techniques at the edge to guarantee the confidentiality, integrity, and availability (CIA) of the framework. 4. At the cloud level, another IDS using MUSE (Merged-Hierarchical-Deep-Learning-System-with-Layer-Reuse) is proposed to protect the system against Man-In-The-Middle attacks, while the data is transferred between IoMT-EDGE-CLOUD. Results: These four modules are developed independently by precisely analyzing dependencies. The performance of IDS in terms of precision is 99% and for the identity-management system, the time required to encrypt and decrypt 256-bit key is 66 milliseconds and 220 milliseconds respectively. The true positive rate is 90.1%, which suggests real-time detection and authentication rate. IDS (2) using MUSE (12-layer) the accuracy is >95%, and it consumes 15.7% to 27.63% less time to train than the smaller four-layer model. Conclusion: Our designed models suit edge devices and cloud-based cybersecurity systems and support the fast diagnosis and care required by critically ill patients in the community.

scholarly journals Identity Management System Model in the Internet of Things

TEM Journal ◽  
2020 ◽  
pp. 1338-1347
Author(s):  
Belkacem Athamena ◽  
Zina Houhamdi
Keyword(s):  
Management System ◽  
Identity Management ◽  
User Requirements ◽  
Sequence Diagrams ◽  
Modelling Language ◽  
Unified Modelling Language ◽  
Defining System ◽  
Identity Management System ◽  
System Requirements ◽  
The Internet Of Things

This paper describes the identity management system (IdMS) by defining system and user requirements. Additionally, it introduces the IdMS concept that approaches the things identity management. Moreover, the paper deeply describes the IdMS features using unified modelling language (UML) diagrams such as class, system, and sequence diagrams to show the main system functionalities. Ultimately, the suggested system is evaluated by comparing it with the existing systems and discussing the fulfilment of user and system requirements.

scholarly journals Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ivandro Ortet Lopes ◽  
Deqing Zou ◽  
Francis A Ruambo ◽  
Saeed Akbar ◽  
Bin Yuan
Keyword(s):  
Deep Learning ◽  
Intrusion Detection System ◽  
State Of The Art ◽  
Detection System ◽  
Denial Of Service ◽  
Model Performance ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Validation Metrics ◽  
High Processing

Distributed Denial of Service (DDoS) is a predominant threat to the availability of online services due to their size and frequency. However, developing an effective security mechanism to protect a network from this threat is a big challenge because DDoS uses various attack approaches coupled with several possible combinations. Furthermore, most of the existing deep learning- (DL-) based models pose a high processing overhead or may not perform well to detect the recently reported DDoS attacks as these models use outdated datasets for training and evaluation. To address the issues mentioned earlier, we propose CyDDoS, an integrated intrusion detection system (IDS) framework, which combines an ensemble of feature engineering algorithms with the deep neural network. The ensemble feature selection is based on five machine learning classifiers used to identify and extract the most relevant features used by the predictive model. This approach improves the model performance by processing only a subset of relevant features while reducing the computation requirement. We evaluate the model performance based on CICDDoS2019, a modern and realistic dataset consisting of normal and DDoS attack traffic. The evaluation considers different validation metrics such as accuracy, precision, F1-Score, and recall to argue the effectiveness of the proposed framework against state-of-the-art IDSs.

scholarly journals Intrusion Detection System for AES Encripted Low-Power IoT Networks

2021 ◽  
Author(s):  
Eduardo De Oliveira Burger Monteiro Luiz ◽  
Alessandro Copetti ◽  
Luciano Bertini ◽  
Juliano Fontoura Kazienko
Keyword(s):  
Low Power ◽  
Detection System ◽  
Denial Of Service ◽  
Security Requirements ◽  
Power Devices ◽  
Dos Attacks ◽  
Reflection Attack ◽  
Ipv6 Protocol ◽  
Iot Devices ◽  
High Level

The introduction of the IPv6 protocol solved the problem of providingaddresses to network devices. With the emergence of the Internetof Things (IoT), there was also the need to develop a protocolthat would assist in connecting low-power devices. The 6LoWPANprotocols were created for this purpose. However, such protocolsinherited the vulnerabilities and threats related to Denial of Service(DoS) attacks from the IPv4 and IPv6 protocols. In this paper, weprepare a network environment for low-power IoT devices usingCOOJA simulator and Contiki operating system to analyze theenergy consumption of devices. Besides, we propose an IntrusionDetection System (IDS) associated with the AES symmetric encryptionalgorithm for the detection of reflection DoS attacks. Thesymmetric encryption has proven to be an appropriate methoddue to low implementation overhead, not incurring in large powerconsumption, and keeping a high level of system security. The maincontributions of this paper are: (i) implementation of a reflectionattack algorithm for IoT devices; (ii) implementation of an intrusiondetection system using AES encryption; (iii) comparison ofthe power consumption in three distinct scenarios: normal messageexchange, the occurrence of a reflection attack, and runningIDS algorithm. Finally, the results presented show that the IDSwith symmetric cryptography meets the security requirements andrespects the energy limits of low-power sensors.

scholarly journals Multi-level authentication protocol for enabling secure communication in IoT

2021 ◽  
Author(s):  
Khushal Singh ◽  
Nanhay Singh
Keyword(s):  
Secure Communication ◽  
Denial Of Service ◽  
Authentication Protocol ◽  
Communication Overhead ◽  
Simulation Environment ◽  
Dos Attacks ◽  
Multi Level ◽  
Two Phases ◽  
Hashing Function ◽  
Registration Phase

Abstract Internet of Things (IoT) is the domain of interest for the researchers at the present with the exponential growth in technology. Security in IoT is a prime factor, which highlights the need for authentication to tackle various attackers and hackers. Authentication is the process that uniquely identifies the incoming user and this paper develops an authentication protocol based on the chebyshev polynomial, hashing function, session password, and Encryption. The proposed authentication protocol is named as, proposed Elliptic, chebyshev, Session password, and Hash function (ECSH)-based multilevel authentication. For authenticating the incoming user, there are two phases, registration and authentication. In the registration phase, the user is registered with the server and Authentication center (AC), and the authentication follows, which is an eight-step criterion. The authentication is duly based on the scale factor of the user and server, session password, and verification messages. The authentication at the eight levels assures the security against various types of attacks and renders secure communication in IoT with minimal communication overhead and packet-loss. The performance of the method is analyzed using black-hole and Denial-of-service (DOS) attacks with 50 and 100 nodes in the simulation environment. The proposed ECSH-based multilevel authentication acquired the maximal detection rate, PDR, and QOS of 15.2%, 35.7895%, and 26.4623%, respectively in the presence of 50 nodes and DOS attacks, whereas the minimal delay of 135.922 ms is acquired in the presence of 100 nodes and DOS attacks.

Novel identity management system using smart blockchain technology

2021 ◽  
Author(s):  
A. Shobanadevi ◽  
Sumegh Tharewal ◽  
Mukesh Soni ◽  
D. Dinesh Kumar ◽  
Ihtiram Raza Khan ◽  
...  
Keyword(s):  
Management System ◽  
Identity Management ◽  
Blockchain Technology ◽  
Identity Management System
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