Biblio

This document takes an in-depth approach to identify WhatsApp's Security risk management, governance and controls. WhatsApp is a communication mobile application that is available on both android and IOS, recently acquired by Facebook and allows us to stay connected. This document identifies all necessary assets, threats, vulnerabilities, and risks to WhatsApp and further provides mitigations and security controls to possibly utilize and secure the application.

With 3.78 billion social media users worldwide in 2021 (48% of the human population), almost 3 billion images are shared daily. At the same time, a consistent evolution of smartphone cameras has led to a photography explosion with 85% of all new pictures being captured using smartphones. However, lately, there has been an increased discussion of privacy concerns when a person being photographed is unaware of the picture being taken or has reservations about the same being shared. These privacy violations are amplified for people with disabilities, who may find it challenging to raise dissent even if they are aware. Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach. Privacy for people with disabilities has so far received comparatively less attention from the AI community. This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A real time Privacy-Preserving AI System) a novel framework to identify sensitive content. Additionally, we curate and annotate a dataset to identify and localize accessibility markers and classify whether an image is sensitive to a featured subject with a disability. We demonstrate that the proposed lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline, trained on face anonymized data. achieves an F1-score of 73.1%.

Many organizations process and store classified data within their computer networks. Owing to the value of data that they hold; such organizations are more vulnerable to targets from adversaries. Accordingly, the sensitive organizations resort to an ‘air-gap’ approach on their networks, to ensure better protection. However, despite the physical and logical isolation, the attackers have successfully manifested their capabilities by compromising such networks; examples of Stuxnet and Agent.btz in view. Such attacks were possible due to the successful manipulation of human beings. It has been observed that to build up such attacks, persistent reconnaissance of the employees, and their data collection often forms the first step. With the rapid integration of social media into our daily lives, the prospects for data-seekers through that platform are higher. The inherent risks and vulnerabilities of social networking sites/apps have cultivated a rich environment for foreign adversaries to cherry-pick personal information and carry out successful profiling of employees assigned with sensitive appointments. With further targeted social engineering techniques against the identified employees and their families, attackers extract more and more relevant data to make an intelligent picture. Finally, all the information is fused to design their further sophisticated attacks against the air-gapped facility for data pilferage. In this regard, the success of the adversaries in harvesting the personal information of the victims largely depends upon the common errors committed by legitimate users while on duty, in transit, and after their retreat. Such errors would keep on repeating unless these are aligned with their underlying human behaviors and weaknesses, and the requisite mitigation framework is worked out.

Any decentralized, biased distributed network is susceptible to the Sybil malicious attack, in which a malicious node masquerades as numerous different nodes, collectively referred to as Sybil nodes, causing the network to become unresponsive. Cloud computing environments are characterized by their loosely linked nature, which means that no node has comprehensive information of the entire system. In order to prevent Sybil attacks in cloud computing systems, it is necessary to detect them as soon as they occur. The network’s ability to function properly A Sybil attacker has the ability to construct. It is necessary to have multiple identities on a single physical device in order to execute a concerted attack on the network or switch between networks identities in order to make the detection process more difficult, and thereby lack of accountability is being promoted throughout the network. The purpose of this study is to Various varieties of Sybil assaults have been documented, including those that occur in Peer-to-peer reputation systems, self-organizing networks, and other similar technologies. The topic of social network systems is discussed. In addition, there are other approaches in which it has been urged over time that they be reduced or eliminated Their potential risks are also thoroughly investigated.

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.

Disinformation, fake news, and unverified rumors spread quickly in online social networks (OSNs) and manipulate people's opinions and decisions about life events. The solid mathematical solutions of the strategic decisions in OSNs have been provided under game theory models, including multiple roles and features. This work proposes a game-theoretic opinion framework to model subjective opinions and behavioral strategies of attackers, users, and a defender. The attackers use information deception models to disseminate disinformation. We investigate how different game-theoretic opinion models of updating people's subject opinions can influence a way for people to handle disinformation. We compare the opinion dynamics of the five different opinion models (i.e., uncertainty, homophily, assertion, herding, and encounter-based) where an opinion is formulated based on Subjective Logic that offers the capability to deal with uncertain opinions. Via our extensive experiments, we observe that the uncertainty-based opinion model shows the best performance in combating disinformation among all in that uncertainty-based decisions can significantly help users believe true information more than disinformation.

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition.

Centrality measures on graphs have found applications in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

To make supply chains sustainable and smart, companies can use information and communication technologies to manage procurement, sourcing, conversion, logistics, and customer relationship management activities. Characterized by profit, people, and planet, the supply chain processes of creating values and managing risks are expected to be digitally transformed. Once digitized, datafied, and networked, supply chains can account for substantial progress towards sustainability. Given the lack of clarity on the concepts of resilience and human rights for the supply chain, especially with the recent advancement of social media, big data, artificial intelligence, and cloud computing, the study conducts a scoping review. To identify the size, scope, and themes, it collected 180 articles from the Web of Science bibliographic database. The bibliometric findings reveal the overall conceptual and intellectual structure, and the gaps for further research and development. The concept of resilience can be enriched, for instance, by the environmental, social, and governance (ESG) concerns. The enriched notion of resilience can also be expressed in digitized, datafied, and networked forms.

Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.

Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.

Background: Most of the people are not medically qualified for studying or understanding the extremity of their diseases or symptoms. This is the place where natural language processing plays a vital role in healthcare. These chatbots collect patients' health data and depending on the data, these chatbot give more relevant data to patients regarding their body conditions and recommending further steps also. Purposes: In the medical field, AI powered healthcare chatbots are beneficial for assisting patients and guiding them in getting the most relevant assistance. Chatbots are more useful for online search that users or patients go through when patients want to know for their health symptoms. Methods: In this study, the health assistant system was developed using Dialogflow application programming interface (API) which is a Google's Natural language processing powered algorithm and the same is deployed on google assistant, telegram, slack, Facebook messenger, and website and mobile app. With this web application, a user can make health requests/queries via text message and might also get relevant health suggestions/recommendations through it. Results: This chatbot acts like an informative and conversational chatbot. This chatbot provides medical knowledge such as disease symptoms and treatments. Storing patients personal and medical information in a database for further analysis of the patients and patients get real time suggestions from doctors. Conclusion: In the healthcare sector AI-powered applications have seen a remarkable spike in recent days. This covid crisis changed the whole healthcare system upside down. So this NLP powered chatbot system reduced office waiting, saving money, time and energy. Patients might be getting medical knowledge and assisting ourselves within their own time and place.

The number of people suffering from mental health issues like depression and anxiety have spiked enormously in recent times. Conversational agents like chatbots have emerged as an effective way for users to express their feelings and anxious thoughts and in turn obtain some empathetic reply that would relieve their anxiety. In our work, we construct two types of empathetic conversational agent models based on sequence-to-sequence modeling with and without attention mechanism. We implement the attention mechanism proposed by Bahdanau et al. for neural machine translation models. We train our model on the benchmark Facebook Empathetic Dialogue dataset and the BLEU scores are computed. Our empathetic conversational agent model incorporating attention mechanism generates better quality empathetic responses and is better in capturing human feelings and emotions in the conversation.

Privacy and data security are critical aspects in databases, mainly when the latter are publically accessed such in social networks. Furthermore, for advanced databases, such as NoSQL ones, security models and security meta-data must be integrated to the business specification and data. In the literature, the proposed models for NoSQL databases can be considered as static, in the sense where the privileges for a given user are predefined and remain unchanged during job sessions. In this paper, we propose a novel model for NoSQL database access control that we aim that it will be dynamic. To be able to design such model, we have considered the Trust concept to compute the reputation degree for a given user that plays a given role.

Along with technological developments in the mobile environment, mobile devices are used in many areas like banking, social media and communication. The common characteristic of applications in these fields is that they contain personal or financial information of users. These types of applications are developed for Android or IOS operating systems and have become the target of attackers. To detect weakness, security analysts, perform mobile penetration tests using security analysis tools. These analysis tools have advantages and disadvantages to each other. Some tools can prioritize static or dynamic analysis, others not including these types of tests. Within the scope of the current model, we are aim to gather security analysis tools under the penetration testing framework, also contributing analysis results by data fusion algorithm. With the suggested model, security analysts will be able to use these types of analysis tools in addition to using the advantage of fusion algorithms fed by analysis tools outputs.

In the economics environment, Job Matching is always a challenge involving the evolution of knowledge and skills. A good matching of skills and jobs can stimulate the growth of economics. Recommender System (RecSys), as one kind of Job Matching, can help the candidates predict the future job relevant to their preferences. However, RecSys still has the problem of cold start and data sparsity. The content-based filtering in RecSys needs the adaptive data for the specific staffing tasks of Bidirectional Encoder Representations from Transformers (BERT). In this paper, we propose a job RecSys based on skills and locations using a domain-specific Knowledge Graph (KG). This system has three parts: a pipeline of Named Entity Recognition (NER) and Relation Extraction (RE) using BERT; a standardization system for pre-processing, semantic enrichment and semantic similarity measurement; a domain-specific Knowledge Graph (KG). Two different relations in the KG are computed by cosine similarity and Term Frequency-Inverse Document Frequency (TF-IDF) respectively. The raw data used in the staffing RecSys include 3000 descriptions of job offers from Indeed, 126 Curriculum Vitae (CV) in English from Kaggle and 106 CV in French from Linx of Capgemini Engineering. The staffing RecSys is integrated under an architecture of Microservices. The autonomy and effectiveness of the staffing RecSys are verified through the experiment using Discounted Cumulative Gain (DCG). Finally, we propose several potential research directions for this research.

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.

Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline – grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

Digital forensics is concerned with identifying, reporting and responding to security breaches. It is about how to acquire, analyze and report digital evidence and using the technical skills, discovering the traces of Cyber Crime. The field of digital forensics is in high demand due to the constant threats of data breaches and information hacks. Digital Forensics is utilized in the identification and elimination of crimes in any controversy where evidence is preserved in online space. This is the use of specialized techniques for retrieval, authentication and electronic data analysis. Computer forensics deals with the identification, preservation, analysis, documentation and presentation of digital evidence. The paper has analyzed the present-day trends that includes IoT forensics, cloud forensics, network forensics and social media forensics. Recent researches have shown a wide range of threats and cyber-attacks, which requires forensic investigators and forensics scientists to simplify the digital world. Hence, all our research gives a clear view of digital forensics which could be of a great help in forensic investigation. In this research paper we have discussed about the need and way to preserve the digital evidence, so that it is not compromised at any point in time and an unalter evidence can be presented before the court of law.

Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.

Twitter text analysis is quite useful in analysing emotions, sentiments and feedbacks of consumers on products and services. This helps the service providers and the manufacturers to improve their products and services, address serious issues before they lead to a crisis and improve business acumen. Twitter texts also form a data source for various research studies. They are used in topic analysis, sentiment analysis, content analysis and thematic analysis. In this paper, we present a pipeline for searching, analysing and visualizing the text analytics features of twitter texts using web APIs. It allows to build a simple yet powerful twitter text analytics tool for researchers and other interested users.

Nowadays security of shared resources and big data is an important and critical issue. With the growth of information technology and social networks, data and resources are shared in the distributed environment such as cloud and fog computing. Various access control models protect the shared resources from unauthorized users or malicious intruders. Despite the attribute-based access control model that meets the complex security requirement of todays' new computing technologies, considerable anomalies and conflicts in ABAC policies affect the efficiency of the security system. One important and toughest task is policy validation thus to detect and eliminate anomalies and conflicts in policies. Though the previous researches identified anomalies, failed to detect and analyze all considerable anomalies that results vulnerable to hacks and attacks. The primary objective of this paper is to study and analyze the possible anomalies and conflicts in ABAC security policies. We have discussed and analyzed considerable conflicts in policies based on previous researches. This paper can provide a detailed review of anomalies and conflicts in security policies.

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.

With the rapid growth of data sharing through social media networks, determining relevant data items concerning a particular subject becomes paramount. We address the issue of establishing which images represent an event of interest through a semi-supervised learning technique. The method learns consistent and shared features related to an event (from a small set of examples) to propagate them to an unlabeled set. We investigate the behavior of five image feature representations considering low- and high-level features and their combinations. We evaluate the effectiveness of the feature embedding approach on five collected datasets from real-world events.

The continuous increase in sophistication of threat actors over the years has made the use of actionable threat intelligence a critical part of the defence against them. Such Cyber Threat Intelligence is published daily on several online sources, including vulnerability databases, CERT feeds, and social media, as well as on forums and web pages from the Surface and the Dark Web. Named Entity Recognition (NER) techniques can be used to extract the aforementioned information in an actionable form from such sources. In this paper we investigate how the latest advances in the NER domain, and in particular transformer-based models, can facilitate this process. To this end, the dataset for NER in Threat Intelligence (DNRTI) containing more than 300 pieces of threat intelligence reports from open source threat intelligence websites is used. Our experimental results demonstrate that transformer-based techniques are very effective in extracting cybersecurity-related named entities, by considerably outperforming the previous state- of-the-art approaches tested with DNRTI.