Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95 using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93 which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Mohammad propose a novel self-structuring neural network for detecting phishing websites @cite_21 . They specify 17 features, some extracted from third-party service, for 600 legitimate and 800 phishing websites collected from PhishTank and Millersmiles http: www.millersmiles.co.uk archives. Their experiments indicate the high generalizability and ability of the neural network in phishing detection. In another work, they propose a feed forward neural network trained by back propagation to classify websites @cite_20 . 18 features are specified for 859 legitimate and 969 phishing websites, respectively. Jain and Gupta propose a machine learning-based technique using only client-side features to detect phishing websites @cite_23 . They extract 19 features from the URL and source code of the web pages and evaluate their method over 2,141 phishing web pages from PhishTank and Openfish https: openphish.com and 1,918 legitimate pages from Alexa database, some online payment, and banking websites.
@cite_21 @cite_23 @cite_20
2526782364 2462457117 2768347741 2801494564
Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method. Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid-level layers can facilitate the extraction of part-specific features which can be utilized for better recognition performance. This is particularly important in the domain of fine-grained recognition. In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDACNN) for fine-grained classification. The proposed network has two sub-networks: one for detection and one for recognition. The detection sub-network has a novel top-down proposal method to generate small semantic part candidates for detection. The classification sub-network introduces novel part layers that extract features from parts detected by the detection sub-network, and combine them for recognition. As a result, the proposed architecture provides an end-to-end network that performs detection, localization of multiple semantic parts, and whole object recognition within one framework that shares the computation of convolutional filters. Our method outperforms state-of-theart methods with a large margin for small parts detection (e.g. our precision of 93.40 vs the best previous precision of 74.00 for detecting the head on CUB-2011). It also compares favorably to the existing state-of-the-art on finegrained classification, e.g. it achieves 85.14 accuracy on CUB-2011. Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. More specifically, we focus on classification of the well-known convolutional neural networks (CNN). First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train convolutional neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement convolutional neural networks over encrypted data and measure performance of the models. Our experimental results validate the soundness of our approach with several convolutional neural networks with varying number of layers and structures. When applied to the MNIST optical character recognition tasks, our approach achieves 99.52 accuracy which significantly outperforms the state-of-the-art solutions and is very close to the accuracy of the best non-private version, 99.77 . Also, it can make close to 164000 predictions per hour. We also applied our approach to CIFAR-10, which is much more complex compared to MNIST, and were able to achieve 91.5 accuracy with approximation polynomials used as activation functions. These results show that CryptoDL provides efficient, accurate and scalable privacy-preserving predictions. In recent years, security incidents of website occur increasingly frequently, and this motivates us to study websites’ security. Although there are many phishing detection approaches to detect phishing websites, the detection accuracy has not been desirable. In this paper, we propose a novel phishing detection model based on a novel neural network classification method. This detection model can achieve high accu-racy and has good generalization ability by design risk minimization principle. Furthermore, the training process of the novel detection model is simple and stable by Monte Carlo algorithm. Based on testing of a set of phishing and benign websites, we have noted that this novel phishing detection model achieves the best Accuracy, True-positive rate (TPR), False-positive rate (FPR), Precision, Recall, F-measure and Matthews Correlation Coefficient (MCC) comparable to other models as Naive Bayes (NB), Logistic Regression(LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Support Vector Machine (LSVM), Radial-Basis Support Vector Machine (RSVM) and Linear Discriminant Analysis (LDA). Furthermore, based upon experiments, we find that the proposed detection model can achieve a high Accuracy of 97.71 and a low FPR of 1.7 . It indicates that the proposed detection model is promising and can be effectively applied to phishing detection.