scholarly journals Firewall Best Practices for Securing Smart Healthcare Environment: A Review

2021 ◽  
Vol 11 (19) ◽  
pp. 9183
Author(s):  
Raja Waseem Anwar ◽  
Tariq Abdullah ◽  
Flavio Pastore
Keyword(s):  
Best Practices ◽  
Security Measures ◽  
Complex Environments ◽  
Security Risks ◽  
Healthcare Environment ◽  
Smart Healthcare ◽  
Maximum Benefit ◽  
Healthcare Environments ◽  
User Data ◽  
Rapid Pace

Smart healthcare environments are growing at a rapid pace due to the services and benefits offered to healthcare practitioners and to patients. At the same time, smart healthcare environments are becoming increasingly complex environments where a plethora of devices are linked with each other, to deliver services to patients, and they require special security measures to protect the privacy and integrity of user data. Moreover, these environments are exposed to various kinds of security risks, threats, and attacks. Firewalls are considered as the first line of defense for securing smart healthcare networks and addressing the challenges mentioned above. Firewalls are applied at different levels in networks, and range from conventional server-based to cloud-based firewalls. However, the selection and implementation of a proper firewall to get the maximum benefit is a challenging task. Therefore, understanding firewall types, the services offered, and analyzing underlying vulnerabilities are important design considerations that need addressing before implementing a firewall in a smart healthcare environment. The paper provides a comprehensive review and best practices of firewall types, with offered benefits and drawbacks, which may help to define a comprehensive set of policies for smart healthcare devices and environments.

scholarly journals Scoot over Smart Devices: The Invisible Costs of Rental Scooters

2019 ◽  
Vol 17 (1/2) ◽  
pp. 191-197 ◽  
Author(s):  
Andrew Boyles Petersen
Keyword(s):  
North America ◽  
Data Security ◽  
Mobile Apps ◽  
Smart Devices ◽  
Gps Data ◽  
Security Risks ◽  
The Past ◽  
Last Mile ◽  
User Data ◽  
Consumer Data

In the past year, transportation rental companies, including Bird, Lime, and Spin, have dropped hundreds of thousands of rental scooters across North America. Relying on mobile apps and scooter-mounted GPS units, these devices have access to a wide-variety of consumer data, including location, phone number, phone metadata, and more. Pairing corroborated phone and scooter GPS data with a last-mile transportation business model, scooter companies are able to collect a unique, highly identifying dataset on users. Data collected by these companies can be utilized by internal researchers or sold to advertisers and data brokers. Access to so much consumer data, however, poses serious security risks. ­Although Bird, Lime, and Spin posit electric scooters as environmentally friendly and accessible transportation, they also allow for unethical uses of user data through vaguely-worded terms of service. To promote more equitable transportation practices, this article will explore the implications of dockless scooter geotracking, as well as related infrastructure, privacy, and data security ramifications.

Secure Data Validation and Transmission In Cloud and IoT Through Ban Logic And KP-ABE

2021 ◽  
Vol 11 ◽  
Author(s):  
Narander Kumar ◽  
Jitendra Kumar Samriya
Keyword(s):  
Privacy Preservation ◽  
Data Science ◽  
User Authentication ◽  
Ban Logic ◽  
Data Validation ◽  
Security Measures ◽  
Privacy And Security ◽  
Cloud Data ◽  
Control Approach ◽  
User Data

Background: Cloud computing is a service that is being accelerating its growth in the field of information technology in recent years. Privacy and security are challenging issues for cloud users and providers. Obective: This work aims at ensuring secured validation of user and protects data during transmission for users in a public IoT-cloud environment. Existing security measures however fails by their single level of security, adaptability for large amount of data and reliability. Therefore, to overcome these issues and to achieve a better solution for vulnerable data. Method: The suggested method utilizes a secure transmission in cloud using key policy attribute based encryption (KPABE). Initially, user authentication is verified. Then the user data is encrypted with the help of KP-ABE algorithm. Finally, data validation and privacy preservation are done by Burrows-Abadi-Needham (BAN) logic. This verified, and shows that the proposed encryption is correct, secure and efficient to prevent unauthorized access and prevention of data leakage so that less chances of data/identity, theft of a user is the analysis and performed by KP-ABE, that is access control approach. Results: Here the method attains the maximum of 88.35% of validation accuracy with a minimum 8.78ms encryption time, which is better when, compared to the existing methods. The proposed mechanism is done by MATLAB. The performance of the implemented method is calculated based on the time of encryption and decryption, execution time and validation accuracy. Conclusion: Thus the proposed approach attains the high IoT-cloud data security and increases the speed for validation and transmission with high accuracy and used for cyber data science processing.

Insider Threat Prevention, Detection and Mitigation

2009 ◽  
pp. 48-74 ◽  
Author(s):  
Robert F. Mills ◽  
Gilbert L. Peterson ◽  
Michael R. Grimaila
Keyword(s):  
Risk Management ◽  
Best Practices ◽  
Case Studies ◽  
Control Measures ◽  
Insider Threat ◽  
Security Measures ◽  
Ongoing Research ◽  
Unauthorized Access ◽  
Technical Control

The purpose of this chapter is to introduce the insider threat and discuss methods for preventing, detecting, and responding to the threat. Trusted insiders present one of the most significant risks to an organization. They possess elevated privileges when compared to external users, have knowledge about technical and non-technical control measures, and potentially can bypass security measures designed to prevent, detect, or react to unauthorized access. In this chapter, we define the insider threat and summarize various case studies of insider attacks in order to highlight the severity of the problem. We then discuss best practices for preventing, detecting, and mitigating insider attacks, to include application of risk management principles specific to the insider threat. Finally, we provide a survey of ongoing research into detecting irregular activities that are potentially harmful to an organization.

scholarly journals The Security of IP-Based Video Surveillance Systems

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4806
Author(s):  
Naor Kalbo ◽  
Yisroel Mirsky ◽  
Asaf Shabtai ◽  
Yuval Elovici
Keyword(s):  
Best Practices ◽  
Video Surveillance ◽  
The Internet ◽  
Surveillance Systems ◽  
Security Risks ◽  
Industrial Facilities ◽  
Gas Stations ◽  
Systems Security ◽  
Attack Surface ◽  
The Internet Of Things

Over the last decade, video surveillance systems have become a part of the Internet of Things (IoT). These IP-based surveillance systems now protect industrial facilities, railways, gas stations, and even one’s own home. Unfortunately, like other IoT systems, there are inherent security risks which can lead to significant violations of a user’s privacy. In this review, we explore the attack surface of modern surveillance systems and enumerate the various ways they can be compromised with real examples. We also identify the threat agents, their attack goals, attack vectors, and the resulting consequences of successful attacks. Finally, we present current countermeasures and best practices and discuss the threat horizon. The purpose of this review is to provide researchers and engineers with a better understanding of a modern surveillance systems’ security, to harden existing systems and develop improved security solutions.

scholarly journals The role of the healthcare environment in the spread of multidrug-resistant organisms: update on current best practices for containment

2014 ◽  
Vol 2 (3-4) ◽  
pp. 79-90 ◽  
Author(s):  
Roy F. Chemaly ◽  
Sarah Simmons ◽  
Charles Dale ◽  
Shashank S. Ghantoji ◽  
Maria Rodriguez ◽  
...  
Keyword(s):  
Best Practices ◽  
Multidrug Resistant ◽  
Healthcare Environment ◽  
Multidrug Resistant Organisms

scholarly journals Enhanced Secure User Data on Cloud using Cloud Data Centre Computing and Decoy Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 1436-1439 ◽  
Keyword(s):  
Large Set ◽  
Cloud Platform ◽  
Security Risks ◽  
Cloud Data ◽  
Data Centre ◽  
Use Of Data ◽  
User Data ◽  
User Access ◽  
Data Theft ◽  
Malicious Insiders

In recent years the storage of cloud platform was increased a lot. Large set of personal and business data can be stored and accessed at anywhere at any time. Data’s are stored in a secured manner using various secured and optimized algorithms. Over this data theft and modifications are done in a highly manner. Therefore this creates a various security risks and data theft problems in cloud. There is need of security technique to overcome this problem. A proposed technique called cloud data centre technique is a new model for securing data from different attacks and security risks. This technique incorporate the FEBER and CUSCUM algorithms, which will monitor the user activity and then identify the legitimacy and prevent from any unauthorized user access. This data center technique can be used to detect and avoid data theft attacks by malicious insiders. The anonymous and miss use of data also prevented in a very good manner while compared to other techniques

scholarly journals Security in Android

2014 ◽  
Vol 12 (10) ◽  
pp. 3990-3996
Author(s):  
Amritesh Kumar Sharma ◽  
Arun Kumar Singh ◽  
Pankaj P. Singh
Keyword(s):  
Information Security ◽  
Rapid Growth ◽  
New Technologies ◽  
Security Measures ◽  
Security Threat ◽  
Sensitive Data ◽  
Security Risks ◽  
Malicious User ◽  
Security Controls ◽  
Areas Of Concern

New technologies have always created new areas of concern for information security teams. Usually it provides time for the development of effective security controls. The rapid growth of the smartphone in market and the use of these devices for so many sensitive data have led to the emergence of security threat. A malicious user or malware on a device can create a number of risks for an organization, and so the fact that these devices are not necessarily connected does not translate to a lack of security risks. This paper will discuss why it is important to secure an Android device, what some of the potential vulnerabilities are, and security measures that can be introduced to provide a baseline of security of data on Google’s mobile OS

scholarly journals IoT based on secure personal healthcare using RFID technology and steganography

2021 ◽  
Vol 11 (4) ◽  
pp. 3300
Author(s):  
Haider Ali Khan ◽  
Raed Abdulla ◽  
Sathish Kumar Selvaperumal ◽  
Ammar Bathich
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Medical Model ◽  
Environmental Sensors ◽  
Rfid Technology ◽  
Time Data ◽  
Personal Healthcare ◽  
Smart Healthcare ◽  
Real Time Data ◽  
User Data

Internet of things (IoT) makes it attainable for connecting different various smart objects together with the internet. The evolutionary medical model towards medicine can be boosted by IoT with involving sensors such as environmental sensors inside the internal environment of a small room with a specific purpose of monitoring of person's health with a kind of assistance which can be remotely controlled. RF identification (RFID) technology is smart enough to provide personal healthcare providing part of the IoT physical layer through low-cost sensors. Recently researchers have shown more IoT applications in the health service department using RFID technology which also increases real-time data collection. IoT platform which is used in the following research is Blynk and RFID technology for the user's better health analyses and security purposes by developing a two-level secured platform to store the acquired data in the database using RFID and Steganography. Steganography technique is used to make the user data more secure than ever. There were certain privacy concerns which are resolved using this technique. Smart healthcare medical box is designed using SolidWorks health measuring sensors that have been used in the prototype to analyze real-time data.

Electricity Infrastructure Technical Security

2020 ◽  
pp. 1-30
Author(s):  
Martin Hromada ◽  
David Rehak ◽  
Neil Walker
Keyword(s):  
Complex System ◽  
Risk Assessment ◽  
Risk Management ◽  
Risk Analysis ◽  
Best Practices ◽  
Risk Identification ◽  
Security Measures ◽  
Identification Methods ◽  
General Energy ◽  
Electricity Infrastructure

In general, energy infrastructure is a basic but very complex system of elements, interconnections, functional inputs and outputs, which creates the need to break down subsystems, systems, and infrastructure areas. The aim of this chapter is therefore to discuss the possible implementation of approaches to risk assessment and risk management in relation to the application of technical security measures. This chapter of the book will therefore discuss risk analysis methods where the transition from general approaches to risk analysis, through risk identification methods and procedures and the assessment of major industrial and technological risks, to specific risk analysis methodologies for electricity infrastructures, will be presented. An important part of the chapter is also the introduction of practical approaches and methodologies that are accepted as “best practices” in connection with ensuring the technical security of electricity infrastructures.

DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

2021 ◽  
pp. 10-19
Author(s):  
Xuan Tung Truong
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Single Shot ◽  
Complex Environments ◽  
Security Risks ◽  
Security Breaches ◽  
Detection And Tracking ◽  
Illegal Activities ◽  
Fine Tune

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.

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