scholarly journals Blogs, Twitter Feeds, and Reddit Comments: Cross-domain Authorship Attribution

2016 ◽  
Vol 2016 (3) ◽  
pp. 155-171 ◽  
Author(s):  
Rebekah Overdorf ◽  
Rachel Greenstadt
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Security And Privacy ◽  
Authorship Attribution ◽  
The Internet ◽  
Privacy Concern ◽  
Writing Style ◽  
Improve Performance ◽  
Cross Domain ◽  
The Cross

AbstractStylometry is a form of authorship attribution that relies on the linguistic information to attribute documents of unknown authorship based on the writing styles of a suspect set of authors. This paper focuses on the cross-domain subproblem where the known and suspect documents differ in the setting in which they were created. Three distinct domains, Twitter feeds, blog entries, and Reddit comments, are explored in this work. We determine that state-of-the-art methods in stylometry do not perform as well in cross-domain situations (34.3% accuracy) as they do in in-domain situations (83.5% accuracy) and propose methods that improve performance in the cross-domain setting with both feature and classification level techniques which can increase accuracy to up to 70%. In addition to testing these approaches on a large real world dataset, we also examine real world adversarial cases where an author is actively attempting to hide their identity. Being able to identify authors across domains facilitates linking identities across the Internet making this a key security and privacy concern; users can take other measures to ensure their anonymity, but due to their unique writing style, they may not be as anonymous as they believe.

Internet of Things (IoT) Security and Privacy

2018 ◽  
pp. 247-267
Author(s):  
Muawya N. Al Dalaien ◽  
Ameur Bensefia ◽  
Salam A. Hoshang ◽  
Abdul Rahman A. Bathaqili
Keyword(s):  
Internet Of Things ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy Concern ◽  
Computing Systems ◽  
Iot Security ◽  
Privacy Issues ◽  
The Internet Of Things ◽  
Security Concern ◽  
The Way

In recent years the Internet of Things (IoT) has rapidly become a revolutionary technological invention causing significant changes to the way both corporate computing systems, and even household gadgets and appliances, are designed and manufactured. The aim of this chapter is to highlight the security and privacy issues that may affect the evolution of IoT technology. The privacy issues are discussed from customer perspectives: first, the IoT privacy concern where the privacy debates on IoT and the IoT privacy that reflected from users' perspective based on the examination of previous researches results. In addition, the different architectures for IoT are discussed. Finally, the chapter discusses the IoT security concern by collecting, analyzing and presenting the major IoT security concerns in the literature as well as providing some potential solutions to these concerns.

A State-of-the-Art Assessment of US and EU C-ITS Security Solutions

2018 ◽  
pp. 309-337
Author(s):  
Yessenia Berenice Llive ◽  
Norbert Varga ◽  
László Bokor
Keyword(s):  
Secure Communication ◽  
State Of The Art ◽  
Smart Cities ◽  
Security And Privacy ◽  
Transport Systems ◽  
Smart Devices ◽  
The Internet ◽  
Sensitive Data ◽  
Service Oriented ◽  
Art Assessment

In the near future with the innovative services and solutions being currently tested and deployed for cars, homes, offices, transport systems, smart cities, etc., the user connectivity will considerably change. It means that smart devices will be connected to the internet and produce a big impact on the internet traffic, increasing the service demand generated by devices and sensors. However most of these devices are vulnerable to attacks. Hence, the security and privacy become a crucial feature to be included in towards its appropriate deployment. Interconnected, cooperative, service-oriented devices and their related hardware/software solutions will contain sensitive data making such systems susceptible to attacks and leakage of information. Therefore, robust secure communication infrastructures must be established to aid suitable deployment. This chapter is a state-of-the-art assessment of US and EU C-ITS security solutions.

scholarly journals Investigating the factors affecting the internet of things (IOT) adoption model -an exploratory study in Egypt

2020 ◽  
Vol 11 (02) ◽  
pp. 97-108
Author(s):  
Ghada Zaky ◽  
◽  
Prof. Ayman Shawky ◽  
Prof. Mohamed A. Ragheb ◽  
◽  
...  
Keyword(s):  
Developing Countries ◽  
Internet Of Things ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy Concern ◽  
Critical Question ◽  
Iot Applications ◽  
Adoption Model ◽  
The Internet Of Things ◽  
Security Concern

Purpose: This study aims to investigate the factors that affect developing countries especially Egyptian users to adopt IOT Applications/devices, and to what extent those variables affect the overall attitude towards, behavioral intentions and actual usage of IOT applications/devices. Moreover, to develop an extended adoption model by integrating the security concern and privacy concern in UTAUT2 model. Design/Methodology: a descriptive quantitative approach was adopted, and a comprehensive research model was adapted from literature and then tested via an online questionnaire. Results/Findings: The findings revealed that Effort Expectancy, Facilitating Conditions and Security Concern all have a significant positive effect on the overall intention towards using IOT applications/devices in Egypt. Theoretical and practical implications: The Internet of Things (IOT) has emerged as an innovative computing technology destined to optimize business operations and create new products and services. IOT enable the devices to communicate and integrate with one another to collect, exchange, and make data accessible through the Internet. It is projected that by 2025, over 50 billion devices will be connected to the IOT network, reaching a market value of up to $11 trillion. This study adapted the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as the theoretical framework and its findings contribute to the literature by adding security and privacy concerns as new predictor variables. This contributes theoretically to the current literature by providing meaningful insights into the main factors that affect using IOT Applications/Devices in developing countries specially Egypt. This study’s conceptual effort provided an overall overview of how IOT users arrive at choosing (or not choosing) to use IOT applications/devices. Additionally, the analytic effort of this study provided a holistic assessment of the role of each variable. One critical question in this case is whether industrial leaders should assign an equal number of resources to each variable or not. By assessing the significance of each variable, the researcher attempted to clarify the comparative importance of each variable in forecasting purchase intentions and decisions to use IOT applications/devices.

IoT Security and Privacy Concern A systematic Mapping Study

2022 ◽  
Vol 6 (1) ◽  
pp. 0-0
Keyword(s):  
Radio Frequency Identification ◽  
Security And Privacy ◽  
Future Research ◽  
The Internet ◽  
Systematic Mapping Study ◽  
Privacy Concern ◽  
Systematic Mapping ◽  
Iot Security ◽  
Identification Tags ◽  
Use Of The Internet

The extend of clever gadgets has accelerated touchy statistics trade on the Internet the usage of most of the time unsecured channels. Since a large use of RFID (Radio-frequency Identification) tags in the transportation and development industries from 1980 to 1990, with the multiplied use of the Internet with 2G/3G or 4G when you consider that 2000, we are witnessing a new generation of related objects. . This paper aims to understand and additionally grant continuing doe’s research topic, challenge, and Future Direction related to Iot security. A systematic mapping finds out about (SMS) is thus utilized on the way to organize the chosen Articles into the following classification: contribution type, Type of Research, Iot Security, and their approach. We take out an overall of twenty-four Articles in support of this systematic discover out about also they categorize the following described criterion. The findings of this SMS are mentioned and the researcher was once given hints on the possible route for future research

Authorship Attribution for a Resource Poor Language—Urdu

2022 ◽  
Vol 21 (3) ◽  
pp. 1-23
Author(s):  
Zulqarnain Nazir ◽  
Khurram Shahzad ◽  
Muhammad Kamran Malik ◽  
Waheed Anwar ◽  
Imran Sarwar Bajwa ◽  
...  
Keyword(s):  
South Asian ◽  
Real World ◽  
Authorship Attribution ◽  
Writing Style ◽  
Effective Technique ◽  
The Real ◽  
Learning Techniques ◽  
Wide Range ◽  
Heterogeneous Datasets ◽  
Asian Languages

Authorship attribution refers to examining the writing style of authors to determine the likelihood of the original author of a document from a given set of potential authors. Due to the wide range of authorship attribution applications, a plethora of studies have been conducted for various Western, as well as Asian, languages. However, authorship attribution research in the Urdu language has just begun, although Urdu is widely acknowledged as a prominent South Asian language. Furthermore, the existing studies on authorship attribution in Urdu have addressed a considerably easier problem of having less than 20 candidate authors, which is far from the real-world settings. Therefore, the findings from these studies may not be applicable to the real-world settings. To that end, we have made three key contributions: First, we have developed a large authorship attribution corpus for Urdu, which is a low-resource language. The corpus is composed of over 2.6 million tokens and 21,938 news articles by 94 authors, which makes it a closer substitute to the real-world settings. Second, we have analyzed hundreds of stylometry features used in the literature to identify 194 features that are applicable to the Urdu language and developed a taxonomy of these features. Finally, we have performed 66 experiments using two heterogeneous datasets to evaluate the effectiveness of four traditional and three deep learning techniques. The experimental results show the following: (a) Our developed corpus is many folds larger than the existing corpora, and it is more challenging than its counterparts for the authorship attribution task, and (b) Convolutional Neutral Networks is the most effective technique, as it achieved a nearly perfect F1 score of 0.989 for an existing corpus and 0.910 for our newly developed corpus.

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

2020 ◽  
Author(s):  
Yehia I. Alzoubi ◽  
Valmira H. Osmanaj ◽  
Ashraf Jaradat ◽  
Ahmad Al‐Ahmad
Keyword(s):  
State Of The Art ◽  
Fog Computing ◽  
Security And Privacy ◽  
The Internet ◽  
Internet Of Thing

Internet of Things (IoT) Security and Privacy

2021 ◽  
pp. 192-207
Author(s):  
Muawya N. Al Dalaien ◽  
Ameur Bensefia ◽  
Salam A. Hoshang ◽  
Abdul Rahman A. Bathaqili
Keyword(s):  
Internet Of Things ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy Concern ◽  
Computing Systems ◽  
Iot Security ◽  
Privacy Issues ◽  
The Internet Of Things ◽  
Security Concern ◽  
The Way

In recent years the Internet of Things (IoT) has rapidly become a revolutionary technological invention causing significant changes to the way both corporate computing systems, and even household gadgets and appliances, are designed and manufactured. The aim of this chapter is to highlight the security and privacy issues that may affect the evolution of IoT technology. The privacy issues are discussed from customer perspectives: first, the IoT privacy concern where the privacy debates on IoT and the IoT privacy that reflected from users' perspective based on the examination of previous researches results. In addition, the different architectures for IoT are discussed. Finally, the chapter discusses the IoT security concern by collecting, analyzing and presenting the major IoT security concerns in the literature as well as providing some potential solutions to these concerns.

Authorship Attribution for Online Social Media

2018 ◽  
pp. 141-165 ◽  
Author(s):  
Ritu Banga ◽  
Akanksha Bhardwaj ◽  
Sheng-Lung Peng ◽  
Gulshan Shrivastava
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Authorship Attribution ◽  
The Internet ◽  
Writing Style ◽  
Machine Learning Classifiers ◽  
Online Social Media ◽  
Comprehensive Knowledge ◽  
Authorship Identification ◽  
Cyber Ethics

This chapter gives a comprehensive knowledge of various machine learning classifiers to achieve authorship attribution (AA) on short texts, specifically tweets. The need for authorship identification is due to the increasing crime on the internet, which breach cyber ethics by raising the level of anonymity. AA of online messages has witnessed interest from many research communities. Many methods such as statistical and computational have been proposed by linguistics and researchers to identify an author from their writing style. Various ways of extracting and selecting features on the basis of dataset have been reviewed. The authors focused on n-grams features as they proved to be very effective in identifying the true author from a given list of known authors. The study has demonstrated that AA is achievable on the basis of selection criteria of features and methods in small texts and also proved the accuracy of analysis changes according to combination of features. The authors found character grams are good features for identifying the author but are not yet able to identify the author independently.

scholarly journals Multimodal news analytics using measures of cross-modal entity and context consistency

2021 ◽  
Author(s):  
Eric Müller-Budack ◽  
Jonas Theiner ◽  
Sebastian Diering ◽  
Maximilian Idahl ◽  
Sherzod Hakimov ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Multimodal Approach ◽  
Entity Linking ◽  
Fake News ◽  
Misleading Information ◽  
Named Entity ◽  
Multimodal Information ◽  
The World ◽  
The Cross

AbstractThe World Wide Web has become a popular source to gather information and news. Multimodal information, e.g., supplement text with photographs, is typically used to convey the news more effectively or to attract attention. The photographs can be decorative, depict additional details, but might also contain misleading information. The quantification of the cross-modal consistency of entity representations can assist human assessors’ evaluation of the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today’s society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of the entities in text and photograph by exploiting state-of-the-art computer vision approaches. In contrast to previous work, our system automatically acquires example data from the Web and is applicable to real-world news. Moreover, an approach that quantifies contextual image-text relations is introduced. The feasibility is demonstrated on two datasets that cover different languages, topics, and domains.

scholarly journals H-Net: Neural Network for Cross-domain Image Patch Matching

2018 ◽  
Author(s):  
Weiquan Liu ◽  
Xuelun Shen ◽  
Cheng Wang ◽  
Zhihong Zhang ◽  
Chenglu Wen ◽  
...  
Keyword(s):  
3D Model ◽  
State Of The Art ◽  
Image Patch ◽  
Structural Shape ◽  
Cross Domain ◽  
Main Challenge ◽  
Image Dataset ◽  
Patch Matching ◽  
The Cross ◽  
Art Performance

Describing the same scene with different imaging style or rendering image from its 3D model gives us different domain images. Different domain images tend to have a gap and different local appearances, which raise the main challenge on the cross-domain image patch matching. In this paper, we propose to incorporate AutoEncoder into the Siamese network, named as H-Net, of which the structural shape resembles the letter H. The H-Net achieves state-of-the-art performance on the cross-domain image patch matching. Furthermore, we improved H-Net to H-Net++. The H-Net++ extracts invariant feature descriptors in cross-domain image patches and achieves state-of-the-art performance by feature retrieval in Euclidean space. As there is no benchmark dataset including cross-domain images, we made a cross-domain image dataset which consists of camera images, rendering images from UAV 3D model, and images generated by CycleGAN algorithm. Experiments show that the proposed H-Net and H-Net++ outperform the existing algorithms. Our code and cross-domain image dataset are available at https://github.com/Xylon-Sean/H-Net.

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