Feature Classification and Extreme Learning Machine Based Detection of Phishing Websites

Phishing is a cyber-attack that uses a phishing website impersonating a real website to deceive internet users into disclosing sensitive information. Attackers using stolen credentials not only utilize them for the targeted website, but they may also be used to access other famous genuine websites. This paper proposes a novel approach for detecting phishing websites using a feature classification technique and an Extreme Learning Machine (ELM) algorithm. The proposed system extracts various features from the website URL and content, including text-based, image-based, and behavior-based features. These features are then classified using a feature selection technique, which selects the most relevant features to improve the detection accuracy. The selected features are then fed into the ELM algorithm, which is a powerful machine learning method for classifying and predicting data. The ELM algorithm It trains upon a huge set of data legitimate & phishing websites, and final outcome model is applied to classify unknown websites as either legitimate or phishing. The proposed approach is evaluated on several benchmark datasets and compared with other state-of-the-art phishing detection methods. The experimental results demonstrate that the proposed approach achieves high detection accuracy and outperforms other methods in terms of precision, recall, and F1-score. The proposed approach can be used as an effective tool for detecting and preventing phishing attacks, which are a major threat to the security of online users

The Genetic Landscape of Dural Marginal Zone Lymphomas

The dura is a rare site of involvement by marginal zone lymphoma (MZL) and the biology of dural MZL is not well understood. We performed genome-wide DNA copy number and targeted mutational analysis of 14 dural MZL to determine the genetic landscape of this entity. Monoallelic and biallelic inactivation of TNFAIP3 by mutation (n=5) or loss (n=1) was observed in 6/9 (67%) dural MZL exhibiting plasmacytic differentiation, including 3 IgG4+ cases. In contrast, activating NOTCH2 mutations were detected in 4/5 (80%) dural MZL displaying variable monocytoid morphology. Inactivating TBL1XR1 mutations were identified in all NOTCH2 mutated cases. Recurrent mutations in KLHL6 (n=2) and MLL2 (n=2) were also detected. Gains at 6p25.3 (n=2) and losses at 1p36.32 (n=3) were common chromosomal imbalances, with loss of heterozygosity (LOH) of these loci observed in a subset of cases. Translocations involving the IGH or MALT1 genes were not identified. Our results indicate genetic similarities between dural MZL and other MZL subtypes. However, recurrent and mutually exclusive genetic alterations of TNFAIP3 and NOTCH2 appear to be associated with distinct disease phenotypes in dural MZL

The genetic landscape of dural marginal zone lymphomas

The dura is a rare site of involvement by marginal zone lymphoma (MZL) and the biology of dural MZL is not well understood. We performed genome-wide DNA copy number and targeted mutational analysis of 14 dural MZL to determine the genetic landscape of this entity. Monoallelic and biallelic inactivation of TNFAIP3 by mutation (n=5) or loss (n=1) was observed in 6/9 (67%) dural MZL exhibiting plasmacytic differentiation, including 3 IgG4+ cases. In contrast, activating NOTCH2 mutations were detected in 4/5 (80%) dural MZL displaying variable monocytoid morphology. Inactivating TBL1XR1 mutations were identified in all NOTCH2 mutated cases. Recurrent mutations in KLHL6 (n=2) and MLL2 (n=2) were also detected. Gains at 6p25.3 (n=2) and losses at 1p36.32 (n=3) were common chromosomal imbalances, with loss of heterozygosity (LOH) of these loci observed in a subset of cases. Translocations involving the IGH or MALT1 genes were not identified. Our results indicate genetic similarities between dural MZL and other MZL subtypes. However, recurrent and mutually exclusive genetic alterations of TNFAIP3 and NOTCH2 appear to be associated with distinct disease phenotypes in dural MZL