Biblio

Sentiment Analysis (SA) is an approach for detecting subjective information such as thoughts, outlooks, reactions, and emotional state. The majority of previous SA work treats it as a text-classification problem that requires labelled input to train the model. However, obtaining a tagged dataset is difficult. We will have to do it by hand the majority of the time. Another concern is that the absence of sufficient cross-domain portability creates challenging situation to reuse same-labelled data across applications. As a result, we will have to manually classify data for each domain. This research work applies sentiment analysis to evaluate the entire vaccine twitter dataset. The work involves the lexicon analysis using NLP libraries like neattext, textblob and multi class classification using BERT. This word evaluates and compares the results of the machine learning algorithms.

Any software that runs malicious payloads on victims’ computers is referred to as malware. It is an increasing threat that costs people, businesses, and organizations a lot of money. Attacks on security have developed significantly in recent years. Malware may infiltrate both offline and online media, like: chat, SMS, and spam (email, or social media), because it has a built-in defensive mechanism and may conceal itself from antivirus software or even corrupt it. As a result, there is an urgent need to detect and prevent malware before it damages critical assets around the world. In fact, there are lots of different techniques and tools used to combat versus malware. In this paper, the malware samples were analyzing in the Virtual Box environment using in-depth analysis based on reverse engineering using advanced static malware analysis techniques. The results Obtained from malware analysis which represent a set of valuable information, all anti-malware and anti-virus program companies need for in order to update their products.

WhatsApp is one of the rare applications that has managed to become one of the most popular instant messaging applications all over the world. While inherently designed for simple and fast communication, privacy features such as end-to-end encryption have made confidential communication easy for criminals aiming to commit illegal acts. However, as it meets many daily communication and communication needs, it has a great potential to be digital evidence in interpersonal disputes. In this study, in parallel with the potential of WhatsApp application to contain digital evidence, the abuse of this situation and the manipulation method of multimedia files, which may cause wrong decisions by the judicial authorities, are discussed. The dangerous side of this method, which makes the analysis difficult, is that it can be applied by anyone without the need for high-level root authority or any other application on these devices. In addition, it is difficult to detect as no changes can be made in the database during the analysis phase. In this study, a controlled experimental environment was prepared on the example scenario, the manipulation was carried out and the prepared system analysis was included. The results obtained showed that the evidence at the forensic analysis stage is open to misinterpretation.

Metaverse is becoming the new standard for social networks and 3D virtual worlds when Facebook officially rebranded to Metaverse in October 2021. Many relevant technologies are used in the metaverse to offer 3D immersive and customized experiences at the user’s fingertips. Despite the fact that the metaverse receives a lot of attention and advantages, one of the most pressing concerns for its users is the safety of their digital material and data. As a result of its decentralization, immutability, and transparency, blockchain is a possible alternative. Our goal is to conduct a comprehensive assessment of blockchain systems in the metaverse to properly appreciate its function in the metaverse. To begin with, the paper introduces blockchain and the metaverse and explains why it’s necessary for the metaverse to adopt blockchain technology. Aside from these technological considerations, this article focuses on how blockchain-based approaches for the metaverse may be used from a privacy and security standpoint. There are several technological challenegs that need to be addressed for making the metaverse a reality. The influence of blockchain on important key technologies with in metaverse, such as Artifical Intelligence, big data and the Internet-of-Things (IoT) is also examined. Several prominent initiatives are also shown to demonstrate the importance of blockchain technology in the development of metaverse apps and services. There are many possible possibilities for future development and research in the application of blockchain technology in the metaverse.

Metaverse opens up a new social networking paradigm where people can experience a real interactive feeling without physical space constraints. Social interactions are gradually evolving from text combined with pictures and videos to 3-dimensional virtual reality, making the social experience increasingly physical, implying that more metaverse applications with immersive experiences will be developed in the future. However, the increasing data dimensionality and volume for new metaverse applications present a significant challenge in data acquisition, security, and sharing. Furthermore, metaverse applications require high capacity and ultrareliability for the wireless system to guarantee the quality of user experience, which cannot be addressed in the current fifth-generation system. Therefore, reaching the metaverse is dependent on the revolution in the sixth-generation (6G) wireless communication, which is expected to provide low-latency, high-throughput, and secure services. This article provides a comprehensive view of metaverse applications and investigates the fundamental technologies for the 6G toward metaverse.

Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In order to get more feature information and improve the inpainting effect, we therefore propose a Generative Adversarial Network of Skip Connection (SCGAN), which connects the encoder layers and the decoder layers by skip connection in the generator. The coherence and consistency of the image inpainting edges are improved, and the finer features of the image inpainting are refined, simultaneously using the discriminator's local and global double discriminators model. We also employ WGAN-GP loss to enhance model stability during training, prevent model collapse, and increase the variety of inpainting face images. Finally, experiments on the CelebA dataset and the LFW dataset are performed, and the model's performance is assessed using the PSNR and SSIM indices. Our model's face image inpainting is more realistic and coherent than that of other models, and the model training is more reliable.

Over the years, false news has polluted the online media landscape across the world. In this “post-truth” era, the narratives created by false news have now come into fruition through dismantled democracies, disbelief in science, and hyper-polarized societies. Despite increased efforts in fact-checking & labeling, strengthening detection systems, de-platforming powerful users, promoting media literacy and awareness of the issue, false news continues to be spread exponentially. This study models the behaviors of both the victims of false news and the platform in which it is spread— through the system dynamics methodology. The model was used to develop a policy design by evaluating existing and proposed solutions. The results recommended actively countering confirmation bias, restructuring social networking sites’ recommendation algorithms, and increasing public trust in news organizations.

Nowadays, although it is much more convenient to obtain news with social media and various news platforms, the emergence of all kinds of fake news has become a headache and urgent problem that needs to be solved. Currently, the fake news recognition algorithm for fake news mainly uses GCN, including some other niche algorithms such as GRU, CNN, etc. Although all fake news verification algorithms can reach quite a high accuracy with sufficient datasets, there is still room for improvement for unsupervised learning and semi-supervised. This article finds that the accuracy of the GCN method for fake news detection is basically about 85% through comparison with other neural network models, which is satisfactory, and proposes that the current field lacks a unified training dataset, and that in the future fake news detection models should focus more on semi-supervised learning and unsupervised learning.

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.

Fake news is a new phenomenon that promotes misleading information and fraud via internet social media or traditional news sources. Fake news is readily manufactured and transmitted across numerous social media platforms nowadays, and it has a significant influence on the real world. It is vital to create effective algorithms and tools for detecting misleading information on social media platforms. Most modern research approaches for identifying fraudulent information are based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, and newly built datasets and online services. There is a pressing need to develop a viable approach for readily detecting misleading information. The deep learning LSTM and Bi-LSTM model was proposed as a method for detecting fake news, In this work. First, the NLTK toolkit was used to remove stop words, punctuation, and special characters from the text. The same toolset is used to tokenize and preprocess the text. Since then, GLOVE word embeddings have incorporated higher-level characteristics of the input text extracted from long-term relationships between word sequences captured by the RNN-LSTM, Bi-LSTM model to the preprocessed text. The proposed model additionally employs dropout technology with Dense layers to improve the model's efficiency. The proposed RNN Bi-LSTM-based technique obtains the best accuracy of 94%, and 93% using the Adam optimizer and the Binary cross-entropy loss function with Dropout (0.1,0.2), Once the Dropout range increases it decreases the accuracy of the model as it goes 92% once Dropout (0.3).

False news has become widespread in the last decade in political, economic, and social dimensions. This has been aided by the deep entrenchment of social media networking in these dimensions. Facebook and Twitter have been known to influence the behavior of people significantly. People rely on news/information posted on their favorite social media sites to make purchase decisions. Also, news posted on mainstream and social media platforms has a significant impact on a particular country’s economic stability and social tranquility. Therefore, there is a need to develop a deceptive system that evaluates the news to avoid the repercussions resulting from the rapid dispersion of fake news on social media platforms and other online platforms. To achieve this, the proposed system uses the preprocessing stage results to assign specific vectors to words. Each vector assigned to a word represents an intrinsic characteristic of the word. The resulting word vectors are then applied to RNN models before proceeding to the LSTM model. The output of the LSTM is used to determine whether the news article/piece is fake or otherwise.

Recently, social networks have become more popular owing to the capability of connecting people globally and sharing videos, images and various types of data. A major security issue in social media is the existence of fake accounts. It is a phenomenon that has fake accounts that can be frequently utilized by mischievous users and entities, which falsify, distribute, and duplicate fake news and publicity. As the fake news resulted in serious consequences, numerous research works have focused on the design of automated fake accounts and fake news detection models. In this aspect, this study designs a hyperparameter tuned deep learning based automated fake news detection (HDL-FND) technique. The presented HDL-FND technique accomplishes the effective detection and classification of fake news. Besides, the HDLFND process encompasses a three stage process namely preprocessing, feature extraction, and Bi-Directional Long Short Term Memory (BiLSTM) based classification. The correct way of demonstrating the promising performance of the HDL-FND technique, a sequence of replications were performed on the available Kaggle dataset. The investigational outcomes produce improved performance of the HDL-FND technique in excess of the recent approaches in terms of diverse measures.

The rise of social media has brought the rise of fake news and this fake news comes with negative consequences. With fake news being such a huge issue, efforts should be made to identify any forms of fake news however it is not so simple. Manually identifying fake news can be extremely subjective as determining the accuracy of the information in a story is complex and difficult to perform, even for experts. On the other hand, an automated solution would require a good understanding of NLP which is also complex and may have difficulties producing an accurate output. Therefore, the main problem focused on this project is the viability of developing a system that can effectively and accurately detect and identify fake news. Finding a solution would be a significant benefit to the media industry, particularly the social media industry as this is where a large proportion of fake news is published and spread. In order to find a solution to this problem, this project proposed the development of a fake news identification system using deep learning and natural language processing. The system was developed using a Word2vec model combined with a Long Short-Term Memory model in order to showcase the compatibility of the two models in a whole system. This system was trained and tested using two different dataset collections that each consisted of one real news dataset and one fake news dataset. Furthermore, three independent variables were chosen which were the number of training cycles, data diversity and vector size to analyze the relationship between these variables and the accuracy levels of the system. It was found that these three variables did have a significant effect on the accuracy of the system. From this, the system was then trained and tested with the optimal variables and was able to achieve the minimum expected accuracy level of 90%. The achieving of this accuracy levels confirms the compatibility of the LSTM and Word2vec model and their capability to be synergized into a single system that is able to identify fake news with a high level of accuracy.

Deep web refers to sites that cannot be found by search engines and makes up the 96% of the digital world. The dark web is the part of the deep web that can only be accessed through specialised tools and anonymity networks. To avoid monitoring and control, communities that seek for anonymization are moving to the dark web. In this work, we scrape five dark web forums and construct five graphs to model user connections. These networks are then studied and compared using data mining techniques and social network analysis tools; for each community we identify the key actors, we study the social connections and interactions, we observe the small world effect, and we highlight the type of discussions among the users. Our results indicate that only a small subset of users are influential, while the rapid dissemination of information and resources between users may affect behaviours and formulate ideas for future members.

Web evolution and Web 2.0 social media tools facilitate communication and support the online economy. On the other hand, these tools are actively used by extremist, terrorist and criminal groups. These malicious groups use these new communication channels, such as forums, blogs and social networks, to spread their ideologies, recruit new members, market their malicious goods and raise their funds. They rely on anonymous communication methods that are provided by the new Web. This malicious part of the web is called the “dark web”. Dark web analysis became an active research area in the last few decades, and multiple research studies were conducted in order to understand our enemy and plan for counteract. We have conducted a systematic literature review to identify the state-of-art and open research areas in dark web analysis. We have filtered the available research papers in order to obtain the most relevant work. This filtration yielded 28 studies out of 370. Our systematic review is based on four main factors: the research trends used to analyze dark web, the employed analysis techniques, the analyzed artifacts, and the accuracy and confidence of the available work. Our review results have shown that most of the dark web research relies on content analysis. Also, the results have shown that forum threads are the most analyzed artifacts. Also, the most significant observation is the lack of applying any accuracy metrics or validation techniques by most of the relevant studies. As a result, researchers are advised to consider using acceptance metrics and validation techniques in their future work in order to guarantee the confidence of their study results. In addition, our review has identified some open research areas in dark web analysis which can be considered for future research work.

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks show that the HeteDP method is equipped to resist heterogeneous graph privacy leakage with admirable model generalization.

We study how information flows through a multi-robot network in order to better understand how to provide resilience to malicious information. While the notion of global resilience is well studied, one way existing methods provide global resilience is by bringing robots closer together to improve the connectivity of the network. However, large changes in network structure can impede the team from performing other functions such as coverage, where the robots need to spread apart. Our goal is to mitigate the trade-off between resilience and network structure preservation by applying resilience locally in areas of the network where it is needed most. We introduce a metric, Influence, to identify vulnerable regions in the network requiring resilience. We design a control law targeting local resilience to the vulnerable areas by improving the connectivity of robots within these areas so that each robot has at least 2F+1 vertex-disjoint communication paths between itself and the high influence robot in the vulnerable area. We demonstrate the performance of our local resilience controller in simulation and in hardware by applying it to a coverage problem and comparing our results with an existing global resilience strategy. For the specific hardware experiments, we show that our control provides local resilience to vulnerable areas in the network while only requiring 9.90% and 15.14% deviations from the desired team formation compared to the global strategy.

Cyber security is everybody's responsibility. It is the capability of the person to protect or secure the use of cyberspace from cyber-attacks. Cyber security awareness is the combination of both knowing and doing to safeguard one's personal information or assets. Online threats continue to rise in the Philippines which is the focus of this study, to identify the level of cyber security awareness among the students and teachers of Occidental Mindoro State College (OMSC) Philippines. Results shows that the level of cyber security awareness in terms of Knowledge, majority of the students and teachers got the passing score and above however there are almost fifty percent got below the passing score. In terms of Practices, both the teachers and the students need to strengthen the awareness of system and browser updates to boost the security level of the devices used. More than half of the IT students are aware of the basic cyber security protocol but there is a big percentage in the Non-IT students which is to be considered. Majority of the teachers are aware of the basic cyber security protocols however the remaining number must be looked into. There is a need to intensity the awareness of the students in the proper etiquette in using the social media. Boost the basic cyber security awareness training to all students and teachers to avoid cybercrime victims.

Cloud service uses CAPTCHA to protect itself from malicious programs. With the explosive development of AI technology and the emergency of third-party recognition services, the factors that influence CAPTCHA’s security are going to be more complex. In such a situation, evaluating the security of mainstream CAPTCHAs in cloud services is helpful to guide better CAPTCHA design choices for providers. In this paper, we evaluate and analyze the security of 6 mainstream CAPTCHA image designs in public cloud services. According to the evaluation results, we made some suggestions of CAPTCHA image design choices to cloud service providers. In addition, we particularly discussed the CAPTCHA images adopted by Facebook and Twitter. The evaluations are separated into two stages: (i) using AI techniques alone; (ii) using both AI techniques and third-party services. The former is based on open source models; the latter is conducted under our proposed framework: CAPTCHAMix.

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraintsƒ The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

Managing and storing big data is non-trivial for traditional relational databases (RDBMS). Therefore, the NoSQL (Not Only SQL) database management system emerged. It is ca-pable of handling the vast amount and the heterogeneity of data. In this research, we are interested in one of its trending types, the graph database, namely, the Directed Property Graph (DPG). This type of database is powerful in dealing with complex relationships (\$\textbackslashmathrme.\textbackslashmathrmg\$., social networks). However, its sen-sitive and private data must be protected against unauthorized access. This research proposes a security model that aims at exploiting and combining the benefits of Access Control, View-Based, and Query-Rewriting approaches. This is a novel combination for securing DPG.

Aim: To bring off the spam detection in social media using Support Vector Machine (SVM) algorithm and compare accuracy with Artificial Neural Network (ANN) algorithm sample size of dataset is 5489, Initially the dataset contains several messages which includes spam and ham messages 80% messages are taken as training and 20% of messages are taken as testing. Materials and Methods: Classification was performed by KNN algorithm (N=10) for spam detection in social media and the accuracy was compared with SVM algorithm (N=10) with G power 80% and alpha value 0.05. Results: The value obtained in terms of accuracy was identified by ANN algorithm (98.2%) and for SVM algorithm (96.2%) with significant value 0.749. Conclusion: The accuracy of detecting spam using the ANN algorithm appears to be slightly better than the SVM algorithm.

All of us are familiar with the importance of social media in facilitating communication. e-mail is one of the safest social media platforms for online communications and information transfer over the internet. As of now, many people rely on email or communications provided by strangers. Because everyone may send emails or a message, spammers have a great opportunity to compose spam messages about our many hobbies and passions, interests, and concerns. Our internet speeds are severely slowed down by spam, which also collects personal information like our phone numbers from our contact list. There is a lot of work involved in identifying these fraudsters and also identifying spam content. Email spam refers to the practice of sending large numbers of messages via email. The recipient bears the bulk of the cost of spam, therefore it's practically free advertising. Spam email is a form of commercial advertising for hackers that is financially viable due of the low cost of sending email. Anti-spam filters have become increasingly important as the volume of unwanted bulk e-mail (also spamming) grows. We can define a message, if it is a spam or not using this proposed model. Machine learning algorithms can be discussed in detail, and our data sets will be used to test them all, with the goal of identifying the one that is most accurate and precise in its identification of email spam. Society of machine learning techniques for detecting unsolicited mass email and spam.

Now a days there are many online social networks (OSN) which are very popular among Internet users and use this platform for finding new connections, sharing their activities and thoughts. Twitter is such social media platforms which is very popular among this users. Survey says, it has more than 310 million monthly users who are very active and post around 500+ million tweets in a day and this attracts, the spammer or cyber-criminal to misuse this platform for their malicious benefits. Product advertisement, phishing true users, pornography propagation, stealing the trending news, sharing malicious link to get the victims for making money are the common example of the activities of spammers. In Aug-2014, Twitter made public that 8.5% of its active Twitter users (monthly) that is approx. 23+ million users, who have automatically contacted their servers for regular updates. Thus for a spam free environment in twitter, it is greatly required to detect and filter these spammer from the legitimate users. Here in our research paper, effectiveness & features of twitter spam detection, various methods are summarized with their benefits and limitations are presented. [1]

This paper examines audio-based social networking platforms and how their environments can affect the persistence of fake news and mis/disinformation in the whole information ecosystem. This is performed through an exploration of their features and how they compare to that of general-purpose multimodal platforms. A case study on Spotify and its recent issue on free speech and misinformation is the application area of this paper. As a supplementary, a demographic analysis of the current statistics of podcast streamers is outlined to give an overview of the target audience of possible deception attacks in the future. As for the conclusion, this paper confers a recommendation to policymakers and experts in preparing for future mis-affordance of the features in social environments that may unintentionally give the agents of mis/disinformation prowess to create and sow discord and deception.