6 research outputs found

    Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better Accuracy

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    Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4%. This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts

    MIPEP recessive variants cause a syndrome of left ventricular non-compaction, hypotonia, and infantile death

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    Abstract Background Mitochondrial presequence proteases perform fundamental functions as they process about 70 % of all mitochondrial preproteins that are encoded in the nucleus and imported posttranslationally. The mitochondrial intermediate presequence protease MIP/Oct1, which carries out precursor processing, has not yet been established to have a role in human disease. Methods Whole exome sequencing was performed on four unrelated probands with left ventricular non-compaction (LVNC), developmental delay (DD), seizures, and severe hypotonia. Proposed pathogenic variants were confirmed by Sanger sequencing or array comparative genomic hybridization. Functional analysis of the identified MIP variants was performed using the model organism Saccharomyces cerevisiae as the protein and its functions are highly conserved from yeast to human. Results Biallelic single nucleotide variants (SNVs) or copy number variants (CNVs) in MIPEP, which encodes MIP, were present in all four probands, three of whom had infantile/childhood death. Two patients had compound heterozygous SNVs (p.L582R/p.L71Q and p.E602*/p.L306F) and one patient from a consanguineous family had a homozygous SNV (p.K343E). The fourth patient, identified through the GeneMatcher tool, a part of the Matchmaker Exchange Project, was found to have inherited a paternal SNV (p.H512D) and a maternal CNV (1.4-Mb deletion of 13q12.12) that includes MIPEP. All amino acids affected in the patients’ missense variants are highly conserved from yeast to human and therefore S. cerevisiae was employed for functional analysis (for p.L71Q, p.L306F, and p.K343E). The mutations p.L339F (human p.L306F) and p.K376E (human p.K343E) resulted in a severe decrease of Oct1 protease activity and accumulation of non-processed Oct1 substrates and consequently impaired viability under respiratory growth conditions. The p.L83Q (human p.L71Q) failed to localize to the mitochondria. Conclusions Our findings reveal for the first time the role of the mitochondrial intermediate peptidase in human disease. Loss of MIP function results in a syndrome which consists of LVNC, DD, seizures, hypotonia, and cataracts. Our approach highlights the power of data exchange and the importance of an interrelationship between clinical and research efforts for disease gene discovery
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