Suspicious Online Research Articles

Cyber Threats in the Health Sector in the DRC: Risks, Opportunities, Consequences and Preventive Measures

While hackers ravage the world, social media, banking and other accounts are hacked every day, but cyber threats in DRC are not taken very seriously. There is a total negligence on the part of the country's authorities and hospital managers and human lives are in danger. one of the large hospitals of Kinshasa HJ Hospital has always been faced with multiple problems concerning its system. Once I left to get tested, I found a crowd waiting for the results because their system was blocked. It took almost a week to find a solution to this problem. But no statement has been released regarding this issue. lives are in danger; personal data is not protected and no one is complaining. We encounter many diverse cases across the country. With a country of more than 100 million inhabitants, the country cannot escape this scourge. As in many other countries, healthcare facilities in the DRC are increasingly dependent on IT systems to manage patient records, appointments, medical prescriptions and other critical aspects of their operation. However, this increased reliance on information and communications technology (ICT) also exposes healthcare organizations to various online threats, such as cyberattacks, malware, ransomware, and data theft. These threats can have serious consequences, including disruption of healthcare services, disclosure of confidential patient data and even endangering lives. To combat these threats, it is crucial that healthcare facilities in the DRC implement robust cybersecurity measures. This includes raising staff awareness of IT security practices, implementing effective firewalls and antivirus software, regularly backing up data, and training and educating users on how to recognize and report malware. suspicious online activities. Additionally, close collaboration between health authorities, regulators, healthcare providers and cybersecurity experts is essential to develop effective policies and protocols to protect healthcare infrastructure and data from cyber threats. constantly evolving. 1 Published by Maxime Gautier, December 13 2023 We decided to put together this article to be able to enlighten the entire Congolese community, more specifically health establishments, and warn them about the dangers and risks of technology.

Monitor and detect suspicious online transactions

Monitor and detect suspicious online transactions

A language framework for modeling social media account behavior

Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a language framework for modeling social media account behaviors. Words in this framework, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. Languages from the framework are highly flexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive fine-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.

Suspicious online product reviews: An empirical analysis of brand and product characteristics using Amazon data

Concerns over the authenticity of reviews hinder their usefulness. Consumers discount the information they get from reviews, and this particularly harms brands with high ratings. The authors argue that perceived brand strength, brand advertising effort, price, and sales each act as signals that help positive reviews of a brand seem more reliable. They investigate this by studying Amazon.com reviews of branded products from 16 product categories that have the resources and potential desire to advertise. They examine consumer perceptions of review authenticity as perceived by machine learning algorithms trained on human subjects, as well as by direct perceptions of human subjects during validation. The results indicate that having a strong brand and investment in brand advertising, along with price and sales rank, are valuable to managers since they form a certain protection from suspiciousness.

Monitor and detect suspicious online transactions

Monitor and detect suspicious online transactions

Reaching the "Hard-to-Reach" Sexual and Gender Diverse Communities for Population-Based Research in Cancer Prevention and Control: Methods for Online Survey Data Collection and Management.

PurposeAround 5% of United States (U.S.) population identifies as Sexual and Gender Diverse (SGD), yet there is limited research around cancer prevention among these populations. We present multi-pronged, low-cost, and systematic recruitment strategies used to reach SGD communities in New Mexico (NM), a state that is both largely rural and racially/ethnically classified as a “majority-minority” state.MethodsOur recruitment focused on using: (1) Every Door Direct Mail (EDDM) program, by the United States Postal Services (USPS); (2) Google and Facebook advertisements; (3) Organizational outreach via emails to publicly available SGD-friendly business contacts; (4) Personal outreach via flyers at clinical and community settings across NM. Guided by previous research, we provide detailed descriptions on using strategies to check for fraudulent and suspicious online responses, that ensure data integrity.ResultsA total of 27,369 flyers were distributed through the EDDM program and 436,177 impressions were made through the Google and Facebook ads. We received a total of 6,920 responses on the eligibility survey. For the 5,037 eligible respondents, we received 3,120 (61.9%) complete responses. Of these, 13% (406/3120) were fraudulent/suspicious based on research-informed criteria and were removed. Final analysis included 2,534 respondents, of which the majority (59.9%) reported hearing about the study from social media. Of the respondents, 49.5% were between 31-40 years, 39.5% were Black, Hispanic, or American Indian/Alaskan Native, and 45.9% had an annual household income below $50,000. Over half (55.3%) were assigned male, 40.4% were assigned female, and 4.3% were assigned intersex at birth. Transgender respondents made up 10.6% (n=267) of the respondents. In terms of sexual orientation, 54.1% (n=1371) reported being gay or lesbian, 30% (n=749) bisexual, and 15.8% (n=401) queer. A total of 756 (29.8%) respondents reported receiving a cancer diagnosis and among screen-eligible respondents, 66.2% reported ever having a Pap, 78.6% reported ever having a mammogram, and 84.1% reported ever having a colonoscopy. Over half of eligible respondents (58.7%) reported receiving Human Papillomavirus vaccinations.ConclusionStudy findings showcase effective strategies to reach communities, maximize data quality, and prevent the misrepresentation of data critical to improve health in SGD communities.

RadScore: An Automated Technique to Measure Radicalness Score of Online Social Media Users

Social media platforms provide effective mediums for expressing opinions and thoughts on several topics openly. This does protect our right to freedom of speech, however the enormous reach of social media makes it a potential tool for widespread radicalization among the youth, irrespective of the geographical and demographical boundaries. This necessitates the need to effectively identify the content which is a source of mass online radicalization. In order to curb the propagation, security agencies need an automatic radicalization detection mechanism for mining the huge volumes of social media content. In this article, we propose an approach for detecting online radicalized accounts and quantifying the degree to which these user accounts are propagating radical content. We propose to use three novel features, i.e., Similarity to domain, presence of radical content and sentiment to calculate the radicalness score for each online user. Our algorithm uses a CNN-LSTM-based technique to effectively differentiate between radical/non-radical content with an accuracy of 93%. Our empirical results show that radicalness scores for known radicalized websites are higher as compared to the non-radical users. We believe this is a first ever attempt at quantifying the level of radicalization of users using scientific methods which can be very helpful to national security agencies in tracking suspicious online users and stop the spread of anti-national content on social media.

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data.

The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Web attackers aim to propagate malicious links using various techniques to deceive users. They attempt to control victims’ devices or obtain their passwords remotely, thereby acquiring access to bank accounts, financial transactions, or private and sensitive information they trade via the Internet. QR codes are accessible, free, easy to use, and can be scanned through several free apps on smartphones. As there is no standard structure or authentication phase in QR code generation, such codes are vulnerable to suspicious online content embedding, i.e., phishing, Cross-Site Scripting (XSS), and malware. Many studies have highlighted the attacks that may be perpetrated using barcodes, and there are some security countermeasures. Several of these solutions are limited to malicious link detection methods or require knowledge of cryptographic techniques. This study’s main objective is to detect malicious URLs embedded in QR codes. A dataset of 90 000 benign and malicious URLs was collected from various resources, and their lexical properties were extracted. Two computational intelligence models, fuzzy logic and multilayer perceptron artificial neural network (MLP-ANN), were applied and compared. An MLP-ANN was identified as the best classifier for detecting malicious URLs, and a proactive, secure, real-time computational intelligence barcode scanner implementation (BarCI ) against malicious QR code links was proposed based on this classifier. The results demonstrate that this approach enables efficient real-time attack detection with 82.9% accuracy

IEEE Access Special Section: Curbing Crowdturfing in Online Social Networks

As online social networks are reshaping the way users share their daily experiences, and the way business practitioners manage their marketing activities, crowdturfing has been emerging as a potential threat that concerns many users. If no proper measures are taken, it may result in a crisis, such as public opinion being misled. In general, crowdturfing refers to coordinated suspicious online behaviors, such as hiring online posters to propagandize online comments with mendacious contents or to spread gossip about their competitors. Through crowdturfing, the stakeholders can achieve their expected goals of manipulating the public opinion, which would further bring negative effects on both the Internet users and society.

Market Manipulation and Suspicious Stock Recommendations on Social Media

Social media can help investors gather and share information about stock markets. However, it also presents opportunities for fraudsters to spread false or misleading statements in the marketplace. Analyzing millions of messages sent on the social media platform Twitter about small capitalization firms, we find that an abnormally high message activity on social media is associated with a large price increase on the event day and followed by a sharp price reversal over the next week. Our findings are consistent with the patterns of a pump-and-dump scheme, where fraudsters use social media to temporarily inflate the price of small capitalization stocks. To differentiate between the effects of overoptimism by noise traders and the illegal gains of a pump-and-dump scheme, we investigate social interactions between Twitter users through the use of network theory. We identify several clusters of users with suspicious online activity (stock promoters, fake accounts, automatic postings), favoring the manipulation/promotion hypothesis over the behavioral hypothesis.

Insights into Suspicious Online Ratings: Direct Evidence from TripAdvisor

Online ratings and online reputation management are becoming increasingly popular and important. With this increasing importance, attempts to manipulate online reviews through fake reviews have become more prevalent. Suspicious online reviews (ratings) exist on many e-commerce platforms, but these reviews have rarely been observed and reported as manipulation in academic studies using different test methods. In our research, we examine empirical evidence of suspicious online ratings based on 41,572 ratings on TripAdvisor. Applying quantitative analytics, we find three important results: (1) the gap between overall rating and individual ratings does exist and is significant, especially among the lower class hotels; (2) the proportion of suspicious ratings is about 20% at a standard of 0.5; and (3) reviewers who tend to post excellent ratings are less likely to generate big gaps when posting ratings. We offer specific managerial implications for hotel managers on online reputation management and selected suggestions for future research based on the empirical findings.

A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions

With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proved to be necessary to process this type of increasing workload. In this paper, we first present a framework of Real-Time Self-Adaptive Classifier (RT-SAC) for identifying suspicious bidders in online auctions using an incremental neural network approach. Then, we introduce a clustering module that characterizes bidder behaviors in measurable attributes and uses a hierarchical clustering mechanism to create training datasets. The neural network in RT-SAC is initialized with the training datasets, which consist of labeled historical auction data. Once initialized, the network can be trained incrementally to gradually adapt to new bidding data in real time, and thus, it supports efficient detection of suspicious bidders in online auctions. Finally, we utilize a case study to demonstrate how parameters in RT-SAC can be tuned for optimal operations and how our approach can be used to effectively identify suspicious online bidders in real time.

Original Research: Online Social Networking Patterns Among Adolescents, Young Adults, and Sexual Offenders

The use of online social networks like Facebook continues to increase rapidly among all age groups and segments of our society, presenting new opportunities for the exchange of sexual information as well as for potentially unsafe encounters between predators and the vulnerable or young. This study surveyed middle school, high school, and college-age students, as well as sexual offenders, regarding their use of social networking sites in order to provide information to better focus education and prevention efforts from nurses and other health care providers. Written questionnaires asking about various characteristics of participants' use of social networking sites were distributed to each group and filled out by 404 middle school students, 2,077 high school students, 1,284 students drawn from five traditional four-year colleges, and 466 adults who had committed either an Internet sexual offense or a hands-on sexual offense (in some cases both). Notable findings emerging from our analysis of the questionnaire responses included the following: offenders and students both frequent social networking sites, although at the time of the study offenders reported that they preferred Myspace and students that they preferred Facebook; nearly two-thirds of the Internet offenders said they'd initiated the topic of sex in their first chat session; more than half of the Internet offenders disguised their identity when online; most Internet offenders we surveyed said they preferred communicating with teenage girls rather than teenage boys; high school students' experience with "sexting" (sharing nude photos of themselves or others on cell phones or online) differed significantly according to their sex; a small number of students are being threatened and assaulted by people they meet online; avatar sites such as Second Life were used both by students and offenders, with both child molesters and Internet offenders expressing interest in Second Life. The use of the Internet presents relatively new and complex issues related to the safety and privacy of adolescents and young adults, and it's crucial that our understanding keep pace with these changes. Possible nurse-initiated policy recommendations include designing technologies and educational programs to help in the identification of suspicious online behaviors; strengthening Internet filters and privacy options for protecting students online; and school outreach for students who are harassed, threatened, or assaulted as a consequence of meeting someone online.