Adversarial behaviours knowledge area

The technological advancements witnessed by our society in recent decades have brought improvements in our quality of life, but they have also created a number of opportunities for attackers to cause harm. Before the Internet revolution, most crime and malicious activity generally required a victim and a perpetrator to come into physical contact, and this limited the reach that malicious parties had. Technology has removed the need for physical contact to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio

Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page

2003-2007 Report on Hate Crimes and Discrimination Against Arab Americans

Analyzes rates, patterns, and sources of anti-Arab-American hate crimes and discrimination, including detainee abuse, delays in naturalization, and threats; civil liberties concerns; bias in schools; and defamation in the media. Includes case summaries

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

An Contemplated Approach for Criminality Data using Mining Algorithm

We propose an approach for the arrangement and execution of bad behavior area and criminal recognizing confirmation for Indian urban groups using data mining frameworks. Our approach is parceled into six modules, to be particular�information extraction (DE), information preprocessing (DP), grouping, Google outline, characterization and WEKA� execution. To begin with module, DE expels the unstructured wrongdoing dataset from various wrongdoing Web sources, in the midst of the season of 2000� 2018. Second module, DP cleans, facilitates and diminishes the removed wrongdoing data into sorted out 5,038 wrongdoing events. We address these events using 35 predefined wrongdoing attributes. Secure measures are taken for the wrongdoing database accessibility. Rest four modules are useful for bad behavior acknowledgment, criminal recognizing evidence and desire, and bad behavior affirmation, independently. Wrongdoing acknowledgment is explored using k-suggests gathering, which iteratively makes two wrongdoing bundles that rely upon equivalent wrongdoing properties. Google portray observation to k-infers. Criminal conspicuous verification and estimate is dismembered using KNN portrayal. Bad behavior check of our results is done using WEKA�. WEKA� checks an exactness of 93.62 and 93.99 % in the course of action of two bad behavior clusters using picked bad behavior attributes. Our approach contributes in the change of the overall population by helping the looking at workplaces in bad behavior area and guilty parties' recognizing confirmation, and in this way decreasing the bad behavior rates. Wrongdoings are a social unsettling influence and cost the overall population to an awesome degree from various perspectives. Any examination that can help in separating and comprehending wrongdoing speedier pays for itself. Crime data mining has the capacity of extricating helpful data and concealed examples from the substantial wrongdoing informational indexes. The crime data mining challenges are getting to be fortifying open doors for the coming years. Since the writing of crime information mining has expanded energetically as of late, it winds up obligatory to build up a diagram of the cutting edge. This orderly survey centers around crime data mining procedures and innovations utilized as a part of past investigations. The current work is grouped into various classifications and is introduced utilizing perceptions. This paper additionally demonstrates a few difficulties identified with crime data research

Patterns of media coverage repeated in online abuse on high-profile criminal cases

What relationship do the mainstream media have with online abuse on high-profile criminal cases? This article hopes to make a start at answering this question by examining tweets containing the #McCann hashtag, utilised by a highly engaged community of users to comment on all matters related to the disappearance of British child Madeleine McCann. On #McCann, the child’s parents and other players are often singled out as the perpetrators of her disappearance and other crimes, in a blend of harassment, defamation and insults with conspiracy theories, disinformation and a strong anti-establishment vein typical of the posttruth era. Through an experimental digital ethnography blending elements of content and discourse analysis, this research has observed the #McCann conversation and analysed 500 tweets with the hashtag, observing that some of the most offensive theories posted by users on Twitter reprised themes seen in the mainstream media at the time of the disappearance, which resulted in defamation lawsuits by the McCanns and in complaints about unethical reporting at the Leveson Inquiry. This raises questions about the mainstream media’s responsibility and duty of care towards people they report on in the digital age, and showcases a symbiotic yet diffident relationship between anti-establishment online users and traditional news media

Hate Speech Detection in a mix of English and Hindi-English (Code-Mixed) Tweets

With the increasing usage of social networking platforms seen over recent years, there has been an extensive rise in hate speech usage between the users. Hence, Government and social media platforms face lots of responsibility and challenges to control, detect and eliminate massively growing hateful content as early as possible to prevent future criminal acts such as cyber violence and real-life hate crimes. Since Twitter is used globally by people from various backgrounds and nationalities, the platform contains tweets posted in different languages, including code-mixed language, namely Hindi-English. Due to the informal format of tweets with variations in spelling and grammar, hate speech detection is challenging, especially in code-mixed text containing a mixture of different languages. In this paper, we tackle the critical issue of hate speech on social media, with a focus on a mix of English and Hindi-English (code-mixed) text messages (tweets) on Twitter. We perform hate speech classification using the benefits of character-level embedding representations of tweets and Deep Neural Networks (DNN). We built two architectures, namely Convolutional Neural Network (CNN) and a combination of CNN and Long Short-Term Memory (LSTM) algorithms with character-level embedding as an improvement over Elouali et al. (2020)’s work. Both the models were trained using an imbalanced (original) as well as oversampled (balanced) version of the training dataset and were evaluated on the test set. Extensive experimental analysis was performed by tuning the hyperparameters of our models and evaluating their performance in terms of accuracy, efficiency (runtime) and scalability in detecting whether a tweet is hate speech or non-hate. The performance of our proposed models is compared with Elouali et al. (2020)’s model, and it is observed that our method has an improved accuracy and a significantly improved runtime and is scalable. Among our best performing models, CNN-LSTM performed slightly better than CNN with an accuracy of 88.97%

No. 21: Cross-Border Raiding and Community Conflict in the Lesotho-South African Border Zone

Movement backwards and forwards across borders for work is often considered to be the primary form of unauthorized movement in Southern Africa. In southern Lesotho, a new and particularly dangerous form of two-way cross-border movement has become entrenched. This situation warrants the label “crisis”; a crisis which is devastating parts of the countryside in both Lesotho and the northern Eastern Cape Province of South Africa. Media and official attention has focused on the extreme violence which accompanies cross-border stock raiding. This paper seeks to understand the social and economic roots and impacts of cross-border stock theft. Such an analysis is a vital first-step towards the resolution of the conflict since it shows not only why the violence occurs but who stands to benefit from its perpetuation. The analysis is also helpful to understanding the extent to which the existence of an international border is implicated in the cycle and counter-cycle of violence. This paper concludes with an assessment of official reaction, or inaction, on the crisis. The findings are based upon wide-ranging interviews with 147 respondents in 10 villages in southern Lesotho. A complementary study is now recommended on the South African side of the border. The stock theft epidemic is characterized by the following features: Although stock theft is not new to this border zone, it became more widespread, organized and violent in the 1990s. Some 71% of the Basotho stockowners reported having had stock stolen since 1990, many more than once. Over 40% of nonstockowners say they are without animals because of stock theft. Since 1990, 85% of stockowners in the border villages have lost animals to thieves as compared with 49% from non-border villages. Shepherds from border villages also report a higher rate of victimisation (83%) than those further removed from the border (50%). Most cattle and sheep are stolen from cattle posts where they are guarded only by shepherds. Stock is also taken from village kraals and, on occasion, whole villages have been attacked and all the stock driven off. Villagers in all ten villages rate stock theft as a serious problem. Stock thieves come from within Lesotho as well as across the border in South Africa. Basotho stock thieves also carry out raids in South Africa and vice-versa. Gun use is widespread, although South African raiders seem to have greater access to arms. Much of the theft appears to be coordinated by well-organised criminal gangs but reliable information on their composition and organization is difficult to access. Criminal networks in Lesotho and South Africa also cooperate to dispose of stolen animals in the lowlands of Lesotho and as far afield as Port Elizabeth, Durban and Welkom The upsurge in stock theft is clearly related to growing poverty in the region. On both sides of the border, mine retrenchments have hit hard, sending experienced miners home and denying young men access to wage employment. Not only has this exacerbated household and community poverty, but it has provided willing foot-soldiers for stock thieves. Stock raiding produces further impoverishment, insecurity and suspicion, fuelling the escalating cycle of theft and counter-theft. Though not itself in dispute or a source of conflict per se, the Lesotho-South African border plays an essential role in the organization and impact of stock theft. There are significant differences in vulnerability and impact between villages close to the border and those further inland. The international border leads to a distinctive pattern of stock theft. In the simplest scenario, raiders from one side steal from border villages on the other and vice-versa and drive the stock back over the border. The situation becomes more problematic when Basotho stock thieves use the border as a refuge, stealing from Basotho and driving the animals across the border into South Africa to sell or exchange with South African thieves. Cross-border counter-raids to retrieve lost stock and revenge attacks are also common on both sides of the border. South African victims then target Basotho border villages for revenge raids, resulting in great tension and friction between ordinary Basotho and South Africans. The only Lesotho village reporting harmonious cross-border relations borders a white South African farming area. However, white border farmers are not aloof from the conflict. Lesotho police and villagers are adamant that some white South African farmers are implicated in cross-border theft. Stock raiding has major negative impacts on households, communities and cross-border interaction. The impacts also extend to the national economy. In Qacha’s Nek and Quthing districts, production of wool and mohair has fallen significantly in the last 5 years. Livestock holdings have dropped and the numbers of stockless households has increased. Farmers are reluctant to invest in breeding cattle as households debate the merits of getting rid of their cattle. One prominent stock-owner recently lost M200,000 of stock. Stock theft has also had a deleterious effect on agriculture, reducing the availability of oxen for ploughing fields. No one is immune from small-scale and organized raiding. Stock theft, coupled with decreasing agricultural production and increasing unemployment, deepens poverty and desperation. At the household and community level, the research found the following: Nearly 90% of respondents state their household economies have been negatively affected by stock theft. A household’s entire wealth and livelihood can be wiped out in one attack. Escalating stock theft and related violence have profound social consequences, bringing fear and insecurity to ordinary people. People are abandoning their villages and migrating to town and to South Africa to look for work. Community relations have become fraught with tension and suspicion. Nearly half of all stockowners interviewed suspect specific individuals within their own village are involved in the theft of animals – acting either as informants or actual thieves. Invariably it is the poor who are fingered and stigmatised. Communal cooperation such as livestock loaning for ploughing and mafisa (sharing of products) is in steep decline, as are cultural activities and celebrations which involve the slaughter of animals. Cross-border cooperation, activities and initiatives have collapsed and there is considerable animosity and hatred between the communities on either side of the border. Even casual visiting and shopping have all but ceased. Prevention efforts have involved some cross-border cooperation between villages to apprehend thieves and return cattle but these efforts are sporadic and make little dent on the problem. They often also lead to vicious reprisals from stock-theft syndicates. Vigilantism is on the rise in the face of widespread perceptions that the police and the courts on both sides of the border are either ineffectual or corrupt. This paper examines the inadequacies of the policing of the crisis, highlighting the low rates of arrest and prosecution. The difficulties of geography and inadequate resources which hamper effective policing are highlighted. Only in areas where the army is stationed or soldiers patrol the border has there been any marked decrease in theft. The situation is bound to deteriorate further unless there is effective national-level attention and intervention. The low-level civil war in the nearby Tsolo district of South Africa in 1997 was fuelled by a potent mix of poverty, mine retrenchments and stock theft. This conflict could well pale in comparison with the volatile situation building in the southern Lesotho border zone. Here, the same combination of factors are compounded by ethnic and national difference, and the strategic manipulation of borders by stock thieves on both sides. Both governments need to recognize that this local crisis could escalate into a major conflagration and intervene to defuse the situation, calm tensions and work towards effective policing and a political solution. Within Lesotho, the passage of a new Stock Theft Act promises heavy penalties for the shadowy figures involved in organized raiding, provided they can be caught. The institution of a national stock register also seems a step in the right direction though its likely effectiveness is debated. Both the Lesotho and South African governments should acknowledge that a crisis situation exists and that this is a regional problem. Only when national governments, working together with local stakeholders, take the problem seriously and begin cooperating can workable initiatives to halt this devastating social and economic plague be implemented

Refugees Welcome? Online Hate Speech and Sentiments in Twitter in Spain during the Reception of the Boat Aquarius

High-profile events can trigger expressions of hate speech online, which in turn modifies attitudes and offline behavior towards stigmatized groups. This paper addresses the first path of this process using manual and computational methods to analyze the stream of Twitter messages in Spanish around the boat Aquarius (n = 24,254) before and after the announcement of the Spanish government to welcome the boat in June 2018, a milestone for asylum seekers acceptance in the EU and an event that was highly covered by media. It was observed that most of the messages were related to a few topics and had a generally positive sentiment, although a significant part of messages expressed rejection or hate—often supported by stereotypes and lies—towards refugees and migrants and towards politicians. These expressions grew after the announcement of hosting the boat, although the general sentiment of the messages became more positive. We discuss the theoretical, practical, and methodological implications of the study, and acknowledge limitations referred to the examined timeframe and to the preliminary condition of the conclusions

Cyberbullying in Text Content Detection: An Analytical Review

Technological advancements have resulted in an exponential increase in the use of online social networks (OSNs) worldwide. While online social networks provide a great communication medium, they also increase the user's exposure to life-threatening situations such as suicide, eating disorder, cybercrime, compulsive behavior, anxiety, and depression. To tackle the issue of cyberbullying, most existing literature focuses on developing approaches to identifying factors and understanding the textual factors associated with cyberbullying. While most of these approaches have brought great success in cyberbullying research, data availability needed to develop model detection remains a challenge in the research space. This paper conducts a comprehensive literature review to provide an understanding of cyberbullying detection.Comment: 8 pages. Under revie