As a crime of employing technical means to steal sensitive information of users, phishing is currently a critical threat facing the Internet, and losses due to phishing are growing steadily. Feature engineering is important in phishing website detection solutions, but the accuracy of detection critically depends on prior knowledge of features. Moreover, although features extracted from different dimensions are more comprehensive, a drawback is that extracting these features requires a large amount of time. To address these limitations, we propose a multidimensional feature phishing detection approach based on a fast detection method by using deep learning. In the first step, character sequence features of the given URL are extracted and used for quick classification by deep learning, and this step does not require thirdparty assistance or any prior knowledge about phishing. In the second step, we combine URL statistical features, webpage code features, webpage text features, and the quick classification result of deep learning into multidimensional features. The approach can reduce the detection time for setting a threshold. Testing on a dataset containing millions of phishing URLs and legitimate URLs, the accuracy reaches 98.99%, and the false positive rate is only 0.59%. By reasonably adjusting the threshold, the experimental results show that the detection efficiency can be improved. INDEX TERMS Phishing website detection, convolutional neural network, long short-term memory network, semantic feature, machine learning.

Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a progressive state (i.e., pMCI) and a stable state (i.e., sMCI). With the development of deep learning, the convolutional neural networks (CNNs) have made great progress in image recognition using magnetic resonance imaging (MRI) and positron emission tomography (PET) for AD diagnosis. However, due to the limited availability of these imaging data, it is still challenging to effectively use CNNs for AD diagnosis. Toward this end, we design a novel deep learning framework. Specifically, the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) are exploited in our framework. First, we design a 3D-CNN architecture to derive deep feature representation from both MRI and PET. Then, we apply FSBi-LSTM on the hidden spatial information from deep feature maps to further improve its performance. Finally, we validate our method on the AD neuroimaging initiative (ADNI) dataset. Our method achieves average accuracies of 94.82%, 86.36%, and 65.35% for differentiating AD from normal control (NC), pMCI from NC, and sMCI from NC, respectively, and outperforms the related algorithms in the literature.

CD44, the primary receptor for hyaluronic acid, plays an important role in tumor growth and metastasis. CD44-hyaluronic acid interactions can be exploited for targeted delivery of anti-cancer agents specifically to cancer cells. Although various splicing variants of CD44 are expressed on the plasma membrane of cancer cells, the hyaluronic acid binding domain (HABD) is highly conserved among the CD44 splicing variants. Using a novel two-step process, we have identified monothiophosphate-modified aptamers (thioaptamers) that specifically bind to the CD44’s HABD with high affinities. Binding affinities of the selected thioaptamers for the HABD were in the range of 180–295 nM, significantly higher affinity than that of hyaluronic acid (Kd > μM range). The selected thioaptamers bound to CD44 positive human ovarian cancer cell lines (SKOV3, IGROV, and A2780), but failed to bind CD44 negative NIH3T3 cell line. Our results indicated that thio substitution at specific positions of the DNA phosphate backbone result in specific and high affinity binding of thioaptamers to CD44. The selected thioaptamers will be of great interest for further development as a targeting or imaging agent to deliver therapeutic payloads for cancer tissues.

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