Integrated-system to minimizing cyber harassment in kingdom of Saudi Arabia (KSA)

The proposed system framework consists two main databases: Lexicon dictionary and Summarized previous cases, by depending on Sentiment analysis and N-Gram algorithms to match the terms and documents. In the first branch, the judge opens the cyber case and therefore the system will highlight the technical terms automatically. Furthermore, the technical terms matched with Lexicon dictionary will be highlighted. After that, the judge opens the highlighted terms (as links), and description page will be appeared. The description page contains details about the technical terms (definitions, explanations, examples, etc). On the other side, the second branch aims to retrieve the related legal cases (from the database) judged by courts in UK and KSA. The related cases are the most closed cases to the current legal case by inserting keywords based on the current case. The judge benefits from these cases through the judgment issued to give the fair judgment. N-gram algorithm is used to find the related cases because it has smart approach to expect the most closed document and texts. The system provides the judge with laws used in issuing the judgment in KSA and UK courts

Detecting cyberstalking from social media platform(s) using data mining analytics

Cybercrime is an increasing activity that leads to cyberstalking whilst making the use of data mining algorithms to detect or prevent cyberstalking from social media platforms imperative for this study. The aim of this study was to determine the prevalence of cyberstalking on the social media platforms using Twitter. To achieve the objective, machine learning models that perform data mining alongside the security metrics were used to detect cyberstalking from social media platforms. The derived security metrics were used to flag up any suspicious cyberstalking content. Two datasets of detailed tweets were analysed using NVivo and R Programming. The dominant occurrence of cyberstalking was assessed with the induction of fifteen unigrams identified from the preliminary dataset such as “abuse”, “annoying”, “creep or creepy”, “fear”, “follow or followers”, “gender”, “harassment”, “messaging”, “relationships p/p”, “scared”, “stalker”, “technology”, “unwanted”, “victim”, and “violent”. Ordinal regression was used to analyse the use of the fifteen unigrams which were categorised according to degree or relationship/link towards cyberstalking on the platform Twitter. Moreover, two lightweight machine learning algorithms were used for the model performance showcasing cyberstalking indicative content. K Nearest Neighbour and K Means Clustering were both coded in R computer language for the extraction, refined, analysation and visualisation process for this research. Results showed the emotional terms like “bad”, “sad” and “hate” were attached to the unigrams being linked to cyberstalking. Each emotional term was flagged up in correspondence with one of the fifteen unigrams in tweets that correlate cyberstalking indicative content, proving one must accompany the other. K Means Clustering results showed the two terms “bad” and “sad” were shown within 100 percent of the clustering results and the term “hate” was only seen within 60 percent of the results. Results also revealed that the accuracy of the KNN algorithm was up to 40% in predicting key terms-based cyberstalking content in a real Twitter dataset consisting of 1m data points. This study emphasises the continuous relationship between the fifteen unigrams, emotional terms, and tweets within numerous datasets portrayed in this research, and reveals a general picture that cyberstalking indicative content in fact happens on Twitter at a vast rate with the corresponding links or relationships within the detection of cyberstalking

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

A Psychosocial Behavioral Attribution Model: Examining the Relationship Between the “Dark Triad” and Cyber-Criminal Behaviors Impacting Social Networking Sites

This study proposes that individual personality characteristics and behavioral triggering effects come together to motivate online victimization. It draws from psychology’s current understanding of personality traits, attribution theory, and criminological research. This study combines the current computer deviancy and hacker taxonomies with that of the Dark Triad model of personality mapping. Each computer deviant behavior is identified by its distinct dimensions of cyber-criminal behavior (e.g., unethical hacking, cyberbullying, cyberstalking, and identity theft) and analyzed against the Dark Triad personality factors (i.e., narcissism, Machiavellianism, and psychopathy). The goal of this study is to explore whether there are significant relationships among the Dark Triad personality traits and specific cyber-criminal behaviors within social network sites (SNSs). The study targets offensive security engineers and computer deviants from specific hacker conferences and from websites that discuss or promote computer deviant behavior (e.g., hacking). Additional sampling is taken from a general population of SNS users. Using a snowball sampling method, 235 subjects completed an anonymous, self-report survey that includes items measuring computer deviance, personality traits, and demographics. Results yield that there was no significant relationship between Dark Triad and cyber-criminal behaviors defined in the perceived hypotheses. The final chapter of the study summarizes the results and discusses the mechanisms potentially underlying the findings. In the context of achieving the latter objective, exploratory analyses are incorporated and partly relied upon. It also includes a discussion concerning the implications of the findings in terms of providing theoretical insights on the Dark Triad traits and cyber-criminal behaviors more generally

Cyberstalking: a content analysis of gender-based offenses committed online.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.The 21st century has come up with the increased usage of technology and this has been welcomed by cyber stalkers for it has exacerbated cyberstalking. Cyberstalking therefore has grown considerably within the contemporary environment. Cyberstalking entails the inappropriate, uninvited social exchange behaviours initiated by a perpetrator via online or wireless communication technology and devices. Forms of cyberstalking includes sending threatening or obscene electronic emails, harassing in chat rooms, spamming, tracing another person's computer and internet activity, and posting threatening or harassing messages on blogs or through social media. The study utilised qualitative research methods in which documentary search was utilised as the secondary source of data collection. The study therefore gathered that gender based offences have considerably increased online. The study gathered that women (particularly young women aged 18-24) disproportionately experience severe types of cyber harassment, namely cyberstalking and online sexual harassment. The study also gathered that there are a number of ways which have been documented to deal with cybercrime. Raising awareness, setting up and supporting peer-support networks for the eradicating gender based offences committed online and there is need for industry regulations such as punishment from using twitter and YouTube if found to be offensive. The study also gathered that cyber stalkers are motivated by a number of ways. The first category are those that need to fulfil the psychological needs, wishes, or cravings regarding the victim on the part of the perpetrator and the second category are those motivated by the need to instil fear and gain control over the victim. The third group consists of those cyber stalkers who are motivated with the need to seek revenge or punish the victim. And the last group of cyber stalkers are those motivated by the need to build a relationship with the victim. The study therefore recommends for the need to implement cyber stalking regulations within South Africa for the ones that have been acted are not being efficient in combating cyberstalking

Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach

YesSmart cities are shifting the presence of people from physical world to cyber world (cyberspace). Along with the facilities for societies, the troubles of physical world, such as bullying, aggression and hate speech, are also taking their presence emphatically in cyberspace. This paper aims to dig the posts of social media to identify the bullying comments containing text as well as image. In this paper, we have proposed a unified representation of text and image together to eliminate the need for separate learning modules for image and text. A single-layer Convolutional Neural Network model is used with a unified representation. The major findings of this research are that the text represented as image is a better model to encode the information. We also found that single-layer Convolutional Neural Network is giving better results with two-dimensional representation. In the current scenario, we have used three layers of text and three layers of a colour image to represent the input that gives a recall of 74% of the bullying class with one layer of Convolutional Neural Network.Ministry of Electronics and Information Technology (MeitY), Government of Indi

The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami

This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)

University Students’ Perspectives of Visual-based Cyberbullying on Instagram

Researchers have been investigating the cyberbullying phenomenon since the early 21st century. There is a substantial body of cyberbullying studies focused on text-based formats. However, studies revealed that visual-based social media platforms are more powerful than text-based platforms in affecting people’s emotions, causing significant psychological impact. Young adults ages 18-29 use visual-based social media heavily in their daily lives; therefore, visual cyberbullying on various sites has become a critical issue for this generation. Yet, the majority of existing cyberbullying studies focused on age groups under 18. The studies that did investigate this phenomenon among young adults focused mainly on text-based types of cyberbullying. Few studies have investigated visual-based cyberbullying of the adult population. Thus, this dissertation study explored university students’ perspectives of visual-based cyberbullying, with a specific focus on Instagram, because of its popularity. A Holistic Theoretical Framework was proposed to guide the study. This framework is grounded in the Social Ecological Model and the Cognitive-Affective-Behavioral frameworks. This study applied a mixed-method approach to collect data using four techniques: surveys, interviews, visual narrative inquiry, and scans of policy documents. Findings reported in this study have disclosed the nature of visual-based cyberbullying on Instagram as experienced by university students, revealed students’ perspectives of visual-based cyberbullying, unveiled the visual elements from actual incidents narrated by students, generated a novel definition of visual cyberbullying, and illuminated the gap between current university policies and real-world practices regarding the visual-based cyberbullying issue. This study contributes to the cyberbullying theoretical foundation, especially in exploring visual cyberbullying from cognitive, affective, and behavioral perspectives. Furthermore, the study collected visual cyberbullying cases that were crafted and narrated by study participants who witnessed cyberbullying incidents in real life. Future studies and practitioners may benefit from this study by applying the visual cases participants created to inform the design of research instruments and literacy educational materials. In addition, policymakers in higher education may learn from this study about the need to address cyberbullying more effectively in policy documents targeting undergraduate students. This study may also serve as a reference for the definition and examples of visual cyberbullying

Pornographic Deepfakes: The Case for Federal Criminalization of Revenge Porn’s Next Tragic Act

This could happen to you. Like millions of people worldwide, you have uploaded digital photographs of yourself to the internet through social media platforms. Your pictures aren’t sexually explicit or revealing—they depict your daily life, spending time with friends or taking “selfies” on vacation. But then someone decides they don’t like you. Using an app available on any smartphone, this antagonist clips digital images of your face from your innocuous pictures and pastes them seamlessly onto the body of a person engaged in sexually explicit acts. Without your knowledge or consent, you become the “star” of a realistic, pornographic “deepfake.” This hypothetical reflects an emerging phenomenon in sex exploitation cybercrimes—it is the next tragic act in the unauthorized public dissemination of private, sexually explicit photos or videos known as “revenge porn.” Is there anything you can do if someone inserts you into a pornographic deepfake image or video against your will? Is it against the law to create, share, and spread falsified pornography on the internet? At best, the answer to these questions is complicated and uncertain. At worst, the answer is no. Although criminalizing bad acts is the most effective deterrent against bad actors, no federal or state laws currently criminalize the creation or distribution of pornographic deepfakes. And since deepfakes exist in cyberspace, they are not confined to an individual state jurisdiction. This Article is the first to focus on the intersection of the law and pornographic deepfakes and to propose a legislative solution to the harms they unleash. Ultimately, this Article proposes a national response rooted in federal criminal law because everyone, everywhere is a potential deepfake victim—even you

A Rapidly Shifting Landscape : Why Digitized Violence is the Newest Category of Gender-Based Violence

This paper proposes that new research on technology-facilitated violence must shape gender-based violence against women laws. Given the AI revolution, including large language models (“ LLMs ”), and generative artificial intelligence, new technologies continue to create power disparities that help facilitate gender-based violence both online and offline. The paper argues that the veil of anonymity provided by the digital realm facilitates violence ; and the automation capabilities offered by technology amplify the scope and impact of abusive behavior. Although the direct physical act of sexual violence is different from offline violence, there are similarities. Firstly, both acts share the structural gender and intersectional inequities that lie at the root of such conducts in the first place. Secondly, the defense that women and girls are free to exercise the option to leave an abusive online environment denies women’s and girls’ free exercise of rights to assembly and expression in the online public square. In the final analysis, although not all isolated acts of online violence meet a legal threshold, we need to see these acts as a part of a continuum of offline violence that call for new forms of discourse and a dynamic application of international women’s human rights norms into evolving categories of violence