Unbiased phishing detection using domain name based features

2018 Summer.Includes bibliographical references.Internet users are coming under a barrage of phishing attacks of increasing frequency and sophistication. While these attacks have been remarkably resilient against the vast range of defenses proposed by academia, industry, and research organizations, machine learning approaches appear to be a promising one in distinguishing between phishing and legitimate websites. There are three main concerns with existing machine learning approaches for phishing detection. The first concern is there is neither a framework, preferably open-source, for extracting feature and keeping the dataset updated nor an updated dataset of phishing and legitimate website. The second concern is the large number of features used and the lack of validating arguments for the choice of the features selected to train the machine learning classifier. The last concern relates to the type of datasets used in the literature that seems to be inadvertently biased with respect to the features based on URL or content. In this thesis, we describe the implementation of our open-source and extensible framework to extract features and create up-to-date phishing dataset. With having this framework, named Fresh-Phish, we implemented 29 different features that we used to detect whether a given website is legitimate or phishing. We used 26 features that were reported in related work and added 3 new features and created a dataset of 6,000 websites with these features of which 3,000 were malicious and 3,000 were genuine and tested our approach. Using 6 different classifiers we achieved the accuracy of 93% which is a reasonable high in this field. To address the second and third concerns, we put forward the intuition that the domain name of phishing websites is the tell-tale sign of phishing and holds the key to successful phishing detection. We focus on this aspect of phishing websites and design features that explore the relationship of the domain name to the key elements of the website. Our work differs from existing state-of-the-art as our feature set ensures that there is minimal or no bias with respect to a dataset. Our learning model trains with only seven features and achieves a true positive rate of 98% and a classification accuracy of 97%, on sample dataset. Compared to the state-of-the-art work, our per data instance processing and classification is 4 times faster for legitimate websites and 10 times faster for phishing websites. Importantly, we demonstrate the shortcomings of using features based on URLs as they are likely to be biased towards dataset collection and usage. We show the robustness of our learning algorithm by testing our classifiers on unknown live phishing URLs and achieve a higher detection accuracy of 99.7% compared to the earlier known best result of 95% detection rate

A Novel Access Control Model Based on the Structure of Applications

Nowadays, access control has an important role in the management of access to resources in the networks and applications. The establishment of access control in applications is important particularly. Traditional methods of access control, manage the users’ access only at data-centric level. In this paper a new model is presented in which the access control in applications is performed not only at data-centric level but also at component and plug-in levels. By applying the proposed model, the execution of plug-ins or components will be authorized only in the case of enrollment process and in the necessary authorities. In addition, users can access to plug-ins and components only in the case of gaining the necessary authorities. By using the proposed model, the access control can be applied based on both operational needs and applications capabilities accurately

Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment

Learning from the collective knowledge of data dispersed across private sources can provide neural networks with enhanced generalization capabilities. Federated learning, a method for collaboratively training a machine learning model across remote clients, achieves this by combining client models via the orchestration of a central server. However, current approaches face two critical limitations: i) they struggle to converge when client domains are sufficiently different, and ii) current aggregation techniques produce an identical global model for each client. In this work, we address these issues by reformulating the typical federated learning setup: rather than learning a single global model, we learn N models each optimized for a common objective. To achieve this, we apply a weighted distance minimization to model parameters shared in a peer-to-peer topology. The resulting framework, Iterative Parameter Alignment, applies naturally to the cross-silo setting, and has the following properties: (i) a unique solution for each participant, with the option to globally converge each model in the federation, and (ii) an optional early-stopping mechanism to elicit fairness among peers in collaborative learning settings. These characteristics jointly provide a flexible new framework for iteratively learning from peer models trained on disparate datasets. We find that the technique achieves competitive results on a variety of data partitions compared to state-of-the-art approaches. Further, we show that the method is robust to divergent domains (i.e. disjoint classes across peers) where existing approaches struggle.Comment: Published at IEEE Big Data 202

Web Person Name Disambiguation Using Social Links and Enriched Profile Information

In this article, we investigate the problem of cross-document person name disambiguation, which aimed at resolving ambiguities between person names and clustering web documents according to their association to different persons sharing the same name. The majority of previous work often formulated cross-document name disambiguation as a clustering problem. These methods employed various syntactic and semantic features either from the local corpus or distant knowledge bases to compute similarities between entities and group similar entities. However, these approaches show limitations regarding robustness and performance. We propose an unsupervised, graph-based name disambiguation approach to improve the performance and robustness of the state-of-the-art. Our approach exploits both local information extracted from the given corpus, and global information obtained from distant knowledge bases. We show the effectiveness of our approach by testing it on standard WePS datasets. The experimental results are encouraging and show that our proposed method outperforms several baseline methods and also its counterparts. The experiments show that our approach not only improves the performances, but also increases the robustness of name disambiguation

Isolation and characterization of novel phage displayed scFv antibody for human tumor necrosis factor alpha and molecular docking analysis of their interactions

Introduction: Tumor necrosis factor alpha (TNF-α) expression amplifies to excess amounts in several disorders such as rheumatoid arthritis and psoriasis. Although, Anti-TNF biologics have revolutionized the treatment of these autoimmune diseases, formation of anti-drug antibodies (ADA) has dramatically affected their use. The next generation antibodies (e.g. Fab, scFv) have not only reduced resulted immunogenicity, but also proved several benefits including better tumor penetration and more rapid blood clearance.This study highlights the use of phage display for identification of human single chain fragment antibody against disulfide-bonded TNF-α using phage display technology. Methods and Results: Using affinity selection procedures in this study, a scFv antibody clone was isolated from naïve Tomlinson I phage display library that specifically recognizes and binds to TNF-α. The TNF-α recombinant protein was expressed in genetically engineered Escherichia coli SHuffle® T7 Express, for the first time, which is able to express disulfide-bonded recombinant proteins into their correctly folded states. Conclusions: ELISA-based affinity characterization results indicated that the isolated novel 29.2 kDa scFv binds TNF-α with suitable affinity. In silico homology modeling study using ‘ModWeb’ as well as molecular docking study using Hex program confirmed the scFv and TNF-α interactions with a scFv-TNF- α binding energy of around -593 kj/mol which is well in agreement with our ELSIA results. The cloned scFv antibody may potentially be useful for research and therapeutic applications in the future

Wavelet Based Estimation for the Derivative of a Density by Block Thresholding under Random Censorship

We consider wavelet based method for estimating derivatives of a density via block thresholding when the data obtained are randomly right censored. The proposed method is analogous to that of Hall and Patil (1995) for density estimation in the complete data case that has been extended recently by Li (2003, 2008). We find bounds for the $L_2$-loss over a large range of Besov function classes for the resulting estimators. The results of Hall and Patil (1995), Prakasa Rao (1996) and Li (2003, 2008) are obtained as special cases and the performance of proposed estimator is investigated by numerical study

Comparison of various functionally graded femoral prostheses by finite element analysis

This study is focused on finite element analysis of a model comprising femur into which a femoral component of a total hip replacement was implanted. The considered prosthesis is fabricated from a functionally graded material (FGM) comprising a layer of a titanium alloy bonded to a layer of hydroxyapatite. The elastic modulus of the FGM was adjusted in the radial, longitudinal, and longitudinal-radial directions by altering the volume fraction gradient exponent. Four cases were studied, involving two different methods of anchoring the prosthesis to the spongy bone and two cases of applied loading. The results revealed that the FG prostheses provoked more SED to the bone. The FG prostheses carried less stress, while more stress was induced to the bone and cement. Meanwhile, less shear interface stress was stimulated to the prosthesis-bone interface in the noncemented FG prostheses. The cement-bone interface carried more stress compared to the prosthesis-cement interface. Stair climbing induced more harmful effects to the implanted femur components compared to the normal walking by causing more stress. Therefore, stress shielding, developed stresses, and interface stresses in the THR components could be adjusted through the controlling stiffness of the FG prosthesis by managing volume fraction gradient exponent