Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

This work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876, for the completion of the research. Faculty of Informatics and Management, University of Hradec Kralove, SPEV project Grant Number: 2102/2021.Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.Ministry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1Universiti Teknologi Malaysia (UTM) Vot-20H04Malaysia Research University Network (MRUN) 4L876Faculty of Informatics and Management, University of Hradec Kralove, SPEV project 2102/2021

Archaeal Phospholipid Biosynthetic Pathway Reconstructed in Escherichia coli

A part of the biosynthetic pathway of archaeal membrane lipids, comprised of 4 archaeal enzymes, was reconstructed in the cells of Escherichia coli. The genes of the enzymes were cloned from a mesophilic methanogen, Methanosarcina acetivorans, and the activity of each enzyme was confirmed using recombinant proteins. In vitro radioassay showed that the 4 enzymes are sufficient to synthesize an intermediate of archaeal membrane lipid biosynthesis, that is, 2,3-di-O-geranylgeranyl-sn-glycerol-1-phosphate, from precursors that can be produced endogenously in E. coli. Introduction of the 4 genes into E. coli resulted in the production of archaeal-type lipids. Detailed liquid chromatography/electron spray ionization-mass spectrometry analyses showed that they are metabolites from the expected intermediate, that is, 2,3-di-O-geranylgeranyl-sn-glycerol and 2,3-di-O-geranylgeranyl-sn-glycerol-1-phosphoglycerol. The metabolic processes, that is, dephosphorylation and glycerol modification, are likely catalyzed by endogenous enzymes of E. coli

Double SCN5A mutation underlying asymptomatic Brugada syndrome

Objectives: The purpose of this study was to identify risk markers in patients with Brugada syndrome. Background: Patients with Brugada syndrome who experience syncope or aborted sudden death are at high risk for recurrent lethal arrhythmias. The prognosis and therapeutic approaches in asymptomatic individuals with a Brugada-type ECG (asymptomatic Brugada syndrome) are controversial. Methods: We genetically screened 30 asymptomatic probands (29 men and 1 woman; mean age 47.1 years) exhibiting a spontaneous Brugada-type ECG. Family members of patients with Brugada syndrome were excluded from the study. Results: Twenty-nine of 30 patients (96.7%) remained symptom-free for at least 3 years. One patient (case 1) with a family history of sudden death died suddenly during sleep. Ventricular fibrillation was induced by programmed electrical stimulation in 14 of 18 subjects (78%), but none of these 18 subjects developed spontaneous ventricular arrhythmias. Genetic screening failed to identify SCN5A mutations in most cases but demonstrated a novel double missense mutation (K1527R and A1569P) located on the same allele in another asymptomatic subject (case 2). Heterologously expressed mutant Na channels exhibited a negative shift of steady-state inactivation (9.2 mV) and enhanced slow inactivation, suggesting this individual harbors a subclinical channel dysfunction compatible with symptomatic Brugada syndrome. Conclusions: Asymptomatic individuals with a Brugada-type ECG generally have a better prognosis than their symptomatic counterparts, but a subgroup of these individuals may have a poor prognosis. Severe Na channel dysfunction as a result of SCN5A mutations may not be sufficient to cause symptoms or arrhythmias in patients with Brugada syndrome, suggesting unknown factors or modifier genes influence arrhythmogenesis