scholarly journals Investigating the Experience of Social Engineering Victims: Exploratory and User Testing Study

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2709
Author(s):  
Bilikis Banire ◽  
Dena Al Thani ◽  
Yin Yang
Keyword(s):  
Personal Information ◽  
Public Information ◽  
Social Engineering ◽  
Mobile Technologies ◽  
Detection Methods ◽  
User Testing ◽  
Network Applications ◽  
Detection Approach ◽  
Awareness Information ◽  
Social Media Platforms

The advent of mobile technologies and social network applications has led to an increase in malicious scams and social engineering (SE) attacks which are causing loss of money and breaches of personal information. Understanding how SE attacks spread can provide useful information in curbing them. Artificial Intelligence (AI) has demonstrated efficacy in detecting SE attacks, but the acceptability of such a detection approach is yet to be investigated across users with different levels of SE awareness. This paper conducted two studies: (1) exploratory study where qualitative data were collected from 20 victims of SE attacks to inform the development of an AI-based tool for detecting fraudulent messages; and (2) a user testing study with 48 participants with different occupations to determine the detection tool acceptability. Overall, six major themes emerged from the victims’ actions “experiences: reasons for falling for attacks; attack methods; advice on preventing attacks; detection methods; attack context and victims”. The user testing study showed that the AI-based tool was accepted by all users irrespective of their occupation. The categories of users’ occupations can be attributed to the level of SE awareness. Information security awareness should not be limited to organizational levels but extend to social media platforms as public information.

scholarly journals Optical-Based Biosensors and Their Portable Healthcare Devices for Detecting and Monitoring Biomarkers in Body Fluids

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1285
Author(s):  
Anh Tran Tam Pham ◽  
Angus Wallace ◽  
Xinyi Zhang ◽  
Damian Tohl ◽  
Hao Fu ◽  
...  
Keyword(s):  
Medical Devices ◽  
Point Of Care ◽  
Body Fluids ◽  
Optical Measurements ◽  
Mobile Technologies ◽  
Detection Methods ◽  
Portable Devices ◽  
Bodily Fluids ◽  
Clinical Measurements ◽  
Distinct Features

The detection and monitoring of biomarkers in body fluids has been used to improve human healthcare activities for decades. In recent years, researchers have focused their attention on applying the point-of-care (POC) strategies into biomarker detection. The evolution of mobile technologies has allowed researchers to develop numerous portable medical devices that aim to deliver comparable results to clinical measurements. Among these, optical-based detection methods have been considered as one of the common and efficient ways to detect and monitor the presence of biomarkers in bodily fluids, and emerging aggregation-induced emission luminogens (AIEgens) with their distinct features are merging with portable medical devices. In this review, the detection methodologies that use optical measurements in the POC systems for the detection and monitoring of biomarkers in bodily fluids are compared, including colorimetry, fluorescence and chemiluminescence measurements. The current portable technologies, with or without the use of smartphones in device development, that are combined with optical biosensors for the detection and monitoring of biomarkers in body fluids, are also investigated. The review also discusses novel AIEgens used in the portable systems for the detection and monitoring of biomarkers in body fluid. Finally, the potential of future developments and the use of optical detection-based portable devices in healthcare activities are explored.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

scholarly journals EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1497-1505
Author(s):  
R. B. Andrade ◽  
G. A. O. P. Costa ◽  
G. L. A. Mota ◽  
M. X. Ortega ◽  
R. Q. Feitosa ◽  
...  
Keyword(s):  
Global Climate ◽  
Biodiversity Loss ◽  
Semantic Segmentation ◽  
Training Sample ◽  
Training Data ◽  
Detection Methods ◽  
Amazon Forest ◽  
Convolutional Network ◽  
Detection Approach ◽  
Segmentation Methods

Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.

scholarly journals Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7701
Author(s):  
Sayed-Chhattan Shah
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Smart City ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Cloud System ◽  
Multilayer Network ◽  
Network Applications ◽  
Data Rates ◽  
5G Network

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

scholarly journals CONSIDERATIONS OF CRIMINAL LAW AND FORENSIC SCIENCE REGARDING THE ILLEGAL ACCESS TO A COMPUTER SYSTEM

2018 ◽  
Vol 11 (2) ◽  
pp. 49-57
Author(s):  
Adrian Cristian MOISE
Keyword(s):  
Computer System ◽  
Criminal Law ◽  
Personal Information ◽  
Social Engineering ◽  
Criminal Code ◽  
Criminal Offence ◽  
Web Page ◽  
Council Of Europe ◽  
Visual Identity ◽  
The Social

Starting from the provisions of Article 2 of the Council of Europe Convention on Cybercrime and from the provisions of Article 3 of Directive 2013/40/EU on attacks against information systems, the present study analyses how these provisions have been transposed into the text of Article 360 of the Romanian Criminal Code.  Illegal access to a computer system is a criminal offence that aims to affect the patrimony of individuals or legal entities.The illegal access to computer systems is accomplished with the help of the social engineering techniques, the best known technique of this kind is the use of phishing threats. Typically, phishing attacks will lead the recipient to a Web page designed to simulate the visual identity of a target organization, and to gather personal information about the user, the victim having knowledge of the attack.

Social engineering as a threat to personal financial security

2021 ◽  
Vol 17 (1) ◽  
pp. 150-166
Author(s):  
Andrei L. LOMAKIN ◽  
Evgenii Yu. KHRUSTALEV ◽  
Gleb A. KOSTYURIN
Keyword(s):  
Information Security ◽  
Personal Information ◽  
Public Awareness ◽  
Social Engineering ◽  
Training System ◽  
Security Threats ◽  
Security Threat ◽  
Public And Private ◽  
Security Issues ◽  
The Government

Subject. As the socio-economic relationships are getting digitalized so quickly, the society faces more and more instances of cybercrime. To effectively prevent arising threats to personal information security, it is necessary to know key social engineering methods and security activities to mitigate consequences of emerging threats. Objectives. We herein analyze and detect arising information security threats associated with social engineering. We set forth basic guidelines for preventing threats and improving the personal security from social engineering approaches. Methods. The study relies upon methods of systems analysis, synthesis, analogy and generalization. Results. We determined the most frequent instances associated with social engineering, which cause personal information security threats and possible implications. The article outlines guidelines for improving the persona; security from social engineering approaches as an information security threat. Conclusions and Relevance. To make information security threats associated with social engineering less probable, there should be a comprehensive approach implying two strategies. First, the information security protection should be technologically improved, fitted with various data protection, antivirus, anti-fishing software. Second, people should be more aware of information security issues. Raising the public awareness, the government, heads of various departments, top executives of public and private organizations should set an integrated training system for people, civil servants, employees to proliferate the knowledge of information security basics.

Multimodal Mapping of a University’s Formal and Informal Online Brand

2014 ◽  
pp. 120-162
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
New Media ◽  
Public Relations ◽  
Public Information ◽  
Video Sharing ◽  
Ex Post ◽  
University Reputation ◽  
Social Media Platforms ◽  
Media Channels ◽  
The University

With the popularization of the Social Web (or Read-Write Web) and millions of participants in these interactive spaces, institutions of higher education have found it necessary to create online presences to promote their university brands, presence, and reputation. An important aspect of that engagement involves being aware of how their brand is represented informally (and formally) on social media platforms. Universities have traditionally maintained thin channels of formalized communications through official media channels, but in this participatory new media age, the user-generated contents and communications are created independent of the formal public relations offices. The university brand is evolving independently of official controls. Ex-post interventions to protect university reputation and brand may be too little, too late, and much of the contents are beyond the purview of the formal university. Various offices and clubs have institutional accounts on Facebook as well as wide representation of their faculty, staff, administrators, and students online. There are various microblogging accounts on Twitter. Various photo and video contents related to the institution may be found on photo- and video-sharing sites, like Flickr, and there are video channels on YouTube. All this digital content is widely available and may serve as points-of-contact for the close-in to more distal stakeholders and publics related to the institution. A recently available open-source tool enhances the capability for crawling (extracting data) these various social media platforms (through their Application Programming Interfaces or “APIs”) and enables the capture, analysis, and social network visualization of broadly available public information. Further, this tool enables the analysis of previously hidden information. This chapter introduces the application of Network Overview, Discovery and Exploration for Excel (NodeXL) to the empirical and multimodal analysis of a university’s electronic presence on various social media platforms and offers some initial ideas for the analytical value of such an approach.

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