scholarly journals Design and Implementation of a Security Improvement Framework of Zigbee Network for Intelligent Monitoring in IoT Platform

2018 ◽  
Vol 8 (11) ◽  
pp. 2305 ◽  
Author(s):  
S Rana ◽  
Miah Halim ◽  
M. Kabir
Keyword(s):  
Real Time ◽  
Security System ◽  
Human Interaction ◽  
Main Concern ◽  
Security Threats ◽  
Security Attacks ◽  
Intelligent Monitoring ◽  
Iot Platform ◽  
Zigbee Network ◽  
System Administrator

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things, represented as any items from our daily life that are enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems has been the main concern for the research community. In this paper, different security threats of Zigbee networks in the IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office/corporate environment. Our proposed SIF can predict and protect against various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting against several potential security attacks efficiently, enabling a more secure way of intelligent monitoring in the IoT platform.

scholarly journals Design and Implementation of A Security Improvement Framework of Zigbee Network for Intelligent Monitoring in IoT Platform

2018 ◽  
Author(s):  
S M Sohel Rana ◽  
Miah Abdul Halim ◽  
M Humayun Kabir
Keyword(s):  
Real Time ◽  
Security System ◽  
Human Interaction ◽  
Main Concern ◽  
Security Threats ◽  
Security Attacks ◽  
Intelligent Monitoring ◽  
Iot Platform ◽  
Zigbee Network ◽  
System Administrator

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things which are represented as any item from our daily life that is enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems have been the main concern for the research community. In this paper, different security-threats of Zigbee networks in IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office environment. Our proposed SIF can predict and protect various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting several potential security attacks efficiently enabling more secure way of intelligent monitoring.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Real-time, Simulation-based Identification of Cyber-Security Attacks of Industrial Plants

2021 ◽  
Author(s):  
Abhi Patel ◽  
Tim Schenk ◽  
Steffi Knorn ◽  
Heiko Patzlaff ◽  
Dragan Obradovic ◽  
...  
Keyword(s):  
Real Time ◽  
Cyber Security ◽  
Security Attacks ◽  
Industrial Plants ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Simulation Based

scholarly journals ANALISIS SISTEM MONITORING KEAMANAN SERVER DENGAN SMS ALERT BERBASIS SNORT

2020 ◽  
Vol 1 (1) ◽  
pp. 25
Author(s):  
I Komang Krisna Ade Marta ◽  
I Nyoman Buda Hartawan ◽  
I Kadek Susila Satwika
Keyword(s):  
Real Time ◽  
Short Message Service ◽  
Security System ◽  
Short Message ◽  
Important Thing ◽  
Ddos Attack ◽  
Network Load ◽  
Message Service

AbstrakKeamanan server merupakan hal penting yang perlu diberikan perhatian lebih saat melakukan konfigurasi server. Pada umumnya serangan yang terjadi pada server diketahui setelah terjadinya kegagalan pada server dalam memberikan layanan. Pada penelitian ini, dibangun sebuah sistem keamanan server yang dapat melakukan monitoring pada sebuah server ketika terdeteksi adanya aktivitas yang tidak wajar. Pemberitahuan akan dikirimkan melalui SMS (Short Message Service) ke handphone Administrator jaringan. Sistem yang dibangun melakukan pendeteksian intrusi pada server secara realtime menggunakan SNORT. Ketika terjadi akses yang tidak wajar pada server, maka SNORT akan mendeteksi dan mengirimkan informasi terjadinya aktivitas yang tidak wajar ke Administrator jaringan. Sistem ini diujikan dengan lima jenis serangan yakni PING Attack, DoS/DDoS Attack, Port Scanning, Telnet Access dan FTP Access. Parameter yang diamati pada penelitian ini adalah beban aktivitas yang terjadi pada sumber daya server meliputi CPU, Memory (RAM) dan beban jaringan. Hasil penelitian menunjukkan bahwa saat terjadi upaya serangan terhadap server, SNORT dapat menghasilkan alert yang akan disimpan pada log sekaligus dikirimkan ke handphone Administrator melalui SMS. AbstractServer security is an important thing that needs to be given more attention when configuring a server. In general, attacks that occur on the server are known after a failure on the server in providing services. In this study, a server security system was built that could monitor a server when an unusual activity was detected. Notifications will be sent via SMS (Short Message Service) to the network Administrator's smartphone. The system is built to detect intrusions on the server in real time using SNORT. When improper access occurs on the server, SNORT will detect and send information about the occurrence of unusual activity to the network Administrator. This system is tested with five types of attacks namely PING Attack, DoS / DDoS Attack, Port Scanning, Telnet Access and FTP Access. The parameters observed in this study are the activity load that occurs on server resources including CPU, Memory (RAM) and network load. The results showed that when an attempt was made to attack the server, SNORT could produce alerts that would be stored in a log as well as sent to the Administrator's smartphone via SMS.

scholarly journals Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

2020 ◽  
Vol 10 (2) ◽  
pp. 2164
Author(s):  
Fredy Martinez ◽  
Edwar Jacinto ◽  
Fernando Martinez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Time Estimation ◽  
Obstacle Detection ◽  
Stereoscopic Vision ◽  
Human Interaction ◽  
Human Model ◽  
Service Robots ◽  
Indoor Environments ◽  
Time Operation

This paper presents a low cost strategy for real-time estimation of the position of obstacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

2021 ◽  
Vol 17 (4) ◽  
pp. 75-88
Author(s):  
Padmaja Kadiri ◽  
Seshadri Ravala
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Predictive Models ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Threats ◽  
Learning Models ◽  
Security Attacks ◽  
Cloud Service Provider ◽  
Machine Learning Models

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

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