A Composite Trust Model for Secure Routing in Mobile Ad-Hoc Networks

It is imperative to address the issue of secure routing in mobile ad-hoc networks (MANETs) where the nodes seek for cooperative and trusted behaviour from the peer nodes in the absence of well-established infrastructure and centralized authority. Due to the inherent absence of security considerations in the traditional ad-hoc routing protocols, providing security and reliability in the routing of data packets is a major challenge. This work addresses this issue by proposing a composite trust metric based on the concept of social trust and quality-of-service (QoS) trust. Extended from the ad-hoc on-demand distance vector (AODV) routing protocol, we propose an enhanced trust-based model integrated with an attack-pattern discovery mechanism, which attempts to mitigate the adversaries craving to carry out distinct types of packet-forwarding misbehaviours. We present the detailed mode of operations of three distinct adversary models against which the proposed scheme is evaluated. Simulation results under different network conditions depict that the combination of social and QoS trust components provides significant improvement in packet delivery ratio, routing overhead, and energy consumption compared to an existing trust-based scheme

Pathophysiology and Pathology of Acute Kidney Injury in Patients With COVID-19

© 2020 National Kidney Foundation, Inc. Acute kidney injury (AKI) is common among hospitalized patients with Coronavirus Infectious Disease 2019 (COVID-19), with the occurrence of AKI ranging from 0.5% to 80%. The variability in the occurrence of AKI has been attributed to the difference in geographic locations, race/ethnicity, and severity of illness. AKI among hospitalized patients is associated with increased length of stay and in-hospital deaths. Even patients with AKI who survive to hospital discharge are at risk of developing chronic kidney disease or end-stage kidney disease. An improved knowledge of the pathophysiology of AKI in COVID-19 is crucial to mitigate and manage AKI and to improve the survival of patients who developed AKI during COVID-19. The goal of this article is to provide our current understanding of the etiology and the pathophysiology of AKI in the setting of COVID-19

Improving effectiveness of honeypots: predicting targeted destination port numbers during attacks using J48 algorithm

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the J48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world