Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking

Cosmogenic-neutron activation of TeO2 and implications for neutrinoless double- β decay experiments

Flux-averaged cross sections for cosmogenic-neutron activation of natural tellurium were measured using a neutron beam containing neutrons of kinetic energies up to ∼800 MeV and having an energy spectrum similar to that of cosmic-ray neutrons at sea level. Analysis of the radioisotopes produced reveals that Ag110m will be a dominant contributor to the cosmogenic-activation background in experiments searching for neutrinoless double-β decay of Te130, such as the Cryogenic Underground Observatory for Rare Events (CUORE) and the Sudbury Neutrino Observatory Plus (SNO+). An estimate of the cosmogenic-activation background in the CUORE experiment has been obtained using the results of this measurement and cross-section measurements of proton activation of tellurium. Additionally, the measured cross sections in this work are also compared with results from semiempirical cross-section calculations

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset

Every(day) Identities in Forensics: Performing Identities Within the Constraints of Intercollegiate Forensics

Goffman\u27s (1959) dramaturgical theory of identity provides a framework for making sense of complicated, mundane identity performances. Through in-depth interviews and focus groups conducted with intercollegiate forensic co-culture members, the current research builds on Goffman\u27s dramaturgical theory of identity. Crystallization-based analysis showed identity performances are situated within one another like Russian matroyshka (nesting) dolls. Co-cultural expectations produce multi-level professionalism expectations, and overlapping co-cultures mean individuals manage conflicting conventions. Implications are offered for the forensics community, other co-cultures, and identity scholars

A wide variety of businesses and government agencies support the U.S. real estate market. Examples would include sales agents, national lenders, local credit unions, private mortgage and title insurers, and government sponsored entities (Freddie Mac and Fannie Mae), to name a few. The financial performance and overall success of these organizations depends in large part on the health of the overall real estate market. According to the National Association of Home Builders (NAHB), the construction of one single-family home of average size creates the equivalent of nearly 3 new jobs for a year (Greiner, 2015). The economic impact is significant, with residential construction and related activities contributing approximately 5 percent to overall gross domestic product. With these data points in mind, the ability to accurately predict housing trends has become an increasingly important function for organizations engaged in the real estate market. The government bailouts of Freddie Mac and Fannie Mae in July 2008, following the severe housing market collapse which began earlier that year, serve as an example of the risks associated with the housing market. The housing market collapse had left the two firms, which at the time owned or guaranteed about $5 trillion of home loans, in a dangerous and uncertain financial state (Olick, 2018). Countrywide Home Loans, Indy Mac, and Washington Mutual Bank are a few examples of mortgage banks that did not survive the housing market collapse and subsequent recession. In the wake of the financial crisis, businesses within the real estate market have recognized that predicting the direction of real estate is an essential business requirement. A business acquisition by Radian Group, the Philadelphia-based mortgage insurance company, illustrates the importance of predictive modeling for the mortgage industry. In January 2019, Radian Group acquired Five Bridges Advisors, a Maryland-based firm which develops data analytics and econometric predictive models leveraging artificial intelligence and machine learning techniques (Blumenthal, 2019).

This capstone examines the developing issue of money laundering through online gambling sites which are extensions of casinos located within the United States. The online gambling scene is rapidly growing; and these venue will soon become targets for money laundering by criminals, human traffickers, and even terrorists. Internet gambling and online capabilities have become a haven for money laundering activities...internet gambling operations are vulnerable to be used, not only for money laundering, but also criminal activities ranging from terrorist financing to tax evasion” (Fbi Confirms Online Gambling Opens Door To Fraud, Money Laundering; Age Verification Software Ineffective. (2009, Dec 04) This paper will discuss how casinos which host online gambling must focus on protecting their transactions from money laundering. There must be internal controls to “red flag” any suspicious transactions as well as highly trained staff to review such activity. This paper will examine specific areas which online casinos web sites are susceptible to money laundering and offer solutions to identify these transactions

Hypervisor-Based Active Data Protection for Integrity and Confidentiality Of Dynamically Allocated Memory in Windows Kernel

One of the main issues in the OS security is providing trusted code execution in an untrusted environment. During executing, kernel-mode drivers dynamically allocate memory to store and process their data: Windows core kernel structures, users’ private information, and sensitive data of third-party drivers. All this data can be tampered with by kernel-mode malware. Attacks on Windows-based computers can cause not just hiding a malware driver, process privilege escalation, and stealing private data but also failures of industrial CNC machines. Windows built-in security and existing approaches do not provide the integrity and confidentiality of the allocated memory of third-party drivers. The proposed hypervisor-based system (AllMemPro) protects allocated data from being modified or stolen. AllMemPro prevents access to even 1 byte of allocated data, adapts for newly allocated memory in real time, and protects the driver without its source code. AllMemPro works well on newest Windows 10 1709 x64

The control over personal data: True remedy or fairy tale ?

This research report undertakes an interdisciplinary review of the concept of "control" (i.e. the idea that people should have greater "control" over their data), proposing an analysis of this con-cept in the field of law and computer science. Despite the omnipresence of the notion of control in the EU policy documents, scholarly literature and in the press, the very meaning of this concept remains surprisingly vague and under-studied in the face of contemporary socio-technical environments and practices. Beyond the current fashionable rhetoric of empowerment of the data subject, this report attempts to reorient the scholarly debates towards a more comprehensive and refined understanding of the concept of control by questioning its legal and technical implications on data subject\^as agency