Automotive Ethernet architecture and security: challenges and technologies

Vehicle infrastructure must address the challenges posed by today's advances toward connected and autonomous vehicles. To allow for more flexible architectures, high-bandwidth connections and scalability are needed to connect many sensors and electronic control units (ECUs). At the same time, deterministic and low latency is a critical and significant design requirement to support urgent real-time applications in autonomous vehicles. As a recent solution, the time-sensitive network (TSN) was introduced as Ethernet-based amendments in IEEE 802.1 TSN standards to meet those needs. However, it had hurdle to be overcome before it can be used effectively. This paper discusses the latest studies concerning the automotive Ethernet requirements, including transmission delay studies to improve worst-case end-to-end delay and end-to-end jitter. Also, the paper focuses on the securing Ethernet-based in-vehicle networks (IVNs) by reviewing new encryption and authentication methods and approaches

The Assessment of Patient Clinical Outcome: Advantages, Models, Features of an Ideal Model

Background: The assessment of patient clinical outcome focuses on measuring various aspects of the health status of a patient who is under healthcare intervention. Patient clinical outcome assessment is a very significant process in the clinical field as it allows health care professionals to better understand the effectiveness of their health care programs and thus for enhancing the health care quality in general. It is thus vital that a high quality, informative review of current issues regarding the assessment of patient clinical outcome should be conducted. Aims & Objectives: 1) Summarizes the advantages of the assessment of patient clinical outcome; 2) reviews some of the existing patient clinical outcome assessment models namely: Simulation, Markov, Bayesian belief networks, Bayesian statistics and Conventional statistics, and Kaplan-Meier analysis models; and 3) demonstrates the desired features that should be fulfilled by a well-established ideal patient clinical outcome assessment model. Material & Methods: An integrative review of the literature has been performed using the Google Scholar to explore the field of patient clinical outcome assessment. Conclusion: This paper will directly support researchers, clinicians and health care professionals in their understanding of developments in the domain of the assessment of patient clinical outcome, thus enabling them to propose ideal assessment models

Encountering social engineering activities with a novel honeypot mechanism

Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics

Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

Performance Evaluation of an Intelligent and Optimized Machine Learning Framework for Attack Detection

In current decades, the size and complexity of network traffic data have risen significantly, which increases the likelihood of network penetration. One of today's largest advanced security concerns is the botnet. They are the mechanisms behind several online assaults, including Distribute Denial of Service (DDoS), spams, rebate fraudulence, phishing as well as malware attacks. Several methodologies have been created over time to address these issues. Existing intrusion detection techniques have trouble in processing data from speedy networks and are unable to identify recently launched assaults. Ineffective network traffic categorization has been slowed down by repetitive and pointless characteristics. By identifying the critical attributes and removing the unimportant ones using a feature selection approach could indeed reduce the feature space dimensionality and resolve the problem.Therefore, this articledevelops aninnovative network attack recognitionmodel combining an optimization strategy with machine learning framework namely, Grey Wolf with Artificial Bee Colony optimization-based Support Vector Machine (GWABC-SVM) model. The efficient selection of attributes is accomplished using a novel Grey wolf with artificial bee colony optimization approach and finally the Botnet DDoS attack detection is accomplished through Support Vector machine.This articleconducted an experimental assessment of the machine learning approachesfor UNBS-NB 15 and KDD99 databases for Botnet DDoS attack identification. The proposed optimized machine learning (ML) based network attack detection framework is evaluated in the last phase for its effectiveness in detecting the possible threats. The main advantage of employing SVM is that it offers a wide range of possibilities for intrusion detection program development for difficult complicated situations like cloud computing. In comparison to conventional ML-based models, the suggested technique has a better detection rate of 99.62% and is less time-consuming and robust

Exploiting bluetooth vulnerabilities in e-health IoT devices

Internet of Things (IoT) is an interconnected network of heterogeneous things through the Internet. The current and next generation of e-health systems are dependent on IoT devices such as wireless medical sensors. One of the most important applications of IoT devices in the medical field is the usage of these smart devices for emergency healthcare. In the current interconnected world, Bluetooth Technology plays a vital role in communication due to its less resource consumption which suits the IoT architecture and design. However Bluetooth technology does not come without security flaws. In this article, we explore various security threats in Bluetooth communication for e-Health systems and present some examples of the attacks that have been performed on e-Health systems by exploiting the identified vulnerabilities - 2019 Association for Computing Machinery.This publication was made possible by NPRP grant NPRP10-0125-170250 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Secure Cloud-Mediator Architecture for Mobile-Government using RBAC and DUKPT

Smart mobile devices and cloud computing are widely used today. While mobile and portable devices have different capabilities, architectures, operating systems, and communication channels than one another, government data are distributed over heterogeneous systems. This paper proposes a 3-tier mediation framework providing single application to manage all governmental services. The framework is based on private cloud computing for adapting the content of Mobile-Government (M-Government) services using Role-Based Access Control (RBAC) and Derive Unique Key Per Transaction (DUKPT). The 3-layers in the framework are: presence, integration, and homogenization. The presence layer is responsible for adapting the content with regard to four contexts: device, personal, location, and connectivity contexts. The integration layer, which is hosted in a private cloud server, is responsible for integrating heterogeneous data sources. The homogenization layer is responsible for converting data into XML format. The flexibility of the mediation and XML provides an adaptive environment to stream data based on the capabilities of the device that sends the query to the system.</p