Recently a novel DNA virus (Torque Teno Virus (TTV) has been identified in Japan and shown to be associated with elevated aminotransferase s levels after transfusion. However the exact role of TTV in pathogenesis of liver disease is yet to be established. The purpose of this study was to determine the prevalence of TTV in thalassemic patients and its relationship with elevated alanine-aminotransfrase (ALT) and aspartateaminotransfrase (AST). This cross-sectional analysis study was conducted on 452 thallasemic patients. Serums were collected from all of the patients, first ALT and AST levels were determined. Then, after DNA extraction, TTV DNA was amplified and detected using semi-nested PCR, followed by gel electrophoresis. Demographic characteristics and clinical data were collected from each participant for statistical analysis. The findings showed that 160 of 452 (35.4%) samples had TTV DNA detected by PCR. From 160 TTV DNA positive, 98 (61.20%) were female and 62 (38.80%) of them were male (P=0.549). The mean ALT and AST values in TTV positive group were higher than in TTV negative group, and the difference was statistically significant (p<0.0001). The result showed that the prevalence of TTV in thalassemic patients in Jahrom is less than other studies in Iran and the mean ALT and AST values in TTV positive individuals were about 2 times more than in TTV negative individuals.

/pdf/molecular-prevalence-and-clinical-importance-of-torque-teno-2fi9z1ixeg.pdf

Molecular Prevalence and Clinical Importance of Torque Teno Virus Infection in Thalassemia Patients as High Risk Individuals

pdf/cyber-security-in-smart-grid-survey-and-challenges-35zjs0e33f.pdf

Cyber-security in smart grid: Survey and challenges

A transformation is underway in electric power and energy systems (EPESs) to provide clean distributed energy for sustainable global economic growth. Internet of Things (IoT) is at the forefront of this transformation imparting capabilities, such as real-time monitoring, situational awareness and intelligence, control, and cyber security to transform the existing EPES into intelligent cyber-enabled EPES, which is more efficient, secure, reliable, resilient, and sustainable. Additionally, digitizing the electric power ecosystem using IoT improves asset visibility, optimal management of distributed generation, eliminates energy wastage, and create savings. IoT has a significant impact on EPESs and offers several opportunities for growth and development. There are several challenges with the deployment of IoT for EPESs. Viable solutions need to be developed to overcome these challenges to ensure continued growth of IoT for EPESs. The advancements in computational intelligence capabilities can evolve an intelligent IoT system by emulating biological nervous systems with cognitive computation, streaming and distributed analytics including at the edge and device levels. This review paper provides an assessment of the role, impact and challenges of IoT in transforming EPESs.

Review of Internet of Things (IoT) in Electric Power and Energy Systems

Malicious domains are one of the major resources required for adversaries to run attacks over the Internet. Due to the important role of the Domain Name System (DNS), extensive research has been conducted to identify malicious domains based on their unique behavior reflected in different phases of the life cycle of DNS queries and responses. Existing approaches differ significantly in terms of intuitions, data analysis methods as well as evaluation methodologies. This warrants a thorough systematization of the approaches and a careful review of the advantages and limitations of every group.In this article, we perform such an analysis. To achieve this goal, we present the necessary background knowledge on DNS and malicious activities leveraging DNS. We describe a general framework of malicious domain detection techniques using DNS data. Applying this framework, we categorize existing approaches using several orthogonal viewpoints, namely (1) sources of DNS data and their enrichment, (2) data analysis methods, and (3) evaluation strategies and metrics. In each aspect, we discuss the important challenges that the research community should address in order to fully realize the power of DNS data analysis to fight against attacks leveraging malicious domains.

A Survey on Malicious Domains Detection through DNS Data Analysis

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

https://www.mdpi.com/2073-8994/14/6/1095/pdf?version=1653623569

Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

Deceiving entropy based DoS detection

Computers and Internet have evolved into necessary tools for our professional, personal and social lives. As a result of growing dependence, the availability of these systems has become a concern. This concern increases exponentially when considering systems such as smart power grids and public utility services. Therefore, research should be conducted to develop effective ways of detecting system anomalies. Denial of Service attacks are an important problem for the communication systems. Researchers proposed many DDoS detection approaches. In this study, we propose a novel DDoS detection approach: Cusum - Entropy. Our approach performs additional signal processing on the entropy of the packet header field to improve detection efficiency. We tested our approach using operational network traffic and performing DDoS attacks without jeopardizing the operation network. Our results showed that our approach gives high detection and low false positive rates and outperforms the detection approach using the entropy of packet header field.

Cusum - entropy: an efficient method for DDoS attack detection

This paper is a primer on cryptographic accumulators and how to apply them practically. A cryptographic accumulator is a space- and time-efficient data structure used for set-membership tests. Since it is possible to represent any computational problem where the answer is yes or no as a set-membership problem, cryptographic accumulators are invaluable data structures in computer science and engineering. But, to the best of our knowledge, there is neither a concise survey comparing and contrasting various types of accumulators nor a guide for how to apply the most appropriate one for a given application. Therefore, we address that gap by describing cryptographic accumulators while presenting their fundamental and so-called optional properties. We discuss the effects of each property on the given accumulator's performance in terms of space and time complexity, as well as communication overhead.

An Overview of Cryptographic Accumulators.

An Overview of Cryptographic Accumulators

DoS Attack Detection and Mitigation

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