Arabic text classification methods: Systematic literature review of primary studies

Recent research on Big Data proposed and evaluated a number of advanced techniques to gain meaningful information from the complex and large volume of data available on the World Wide Web. To achieve accurate text analysis, a process is usually initiated with a Text Classification (TC) method. Reviewing the very recent literature in this area shows that most studies are focused on English (and other scripts) while attempts on classifying Arabic texts remain relatively very limited. Hence, we intend to contribute the first Systematic Literature Review (SLR) utilizing a search protocol strictly to summarize key characteristics of the different TC techniques and methods used to classify Arabic text, this work also aims to identify and share a scientific evidence of the gap in current literature to help suggesting areas for further research. Our SLR explicitly investigates empirical evidence as a decision factor to include studies, then conclude which classifier produced more accurate results. Further, our findings identify the lack of standardized corpuses for Arabic text; authors compile their own, and most of the work is focused on Modern Arabic with very little done on Colloquial Arabic despite its wide use in Social Media Networks such as Twitter. In total, 1464 papers were surveyed from which 48 primary studies were included and analyzed

Web browser artefacts in private and portable modes: a forensic investigation

Web browsers are essential tools for accessing the internet. Extra complexities are added to forensic investigations when recovering browsing artefacts as portable and private browsing are now common and available in popular web browsers. Browsers claim that whilst operating in private mode, no data is stored on the system. This paper investigates whether the claims of web browsers discretion are true by analysing the remnants of browsing left by the latest versions of Internet Explorer, Chrome, Firefox, and Opera when used in a private browsing session, as a portable browser, and when the former is running in private mode. Some of our key findings show how forensic analysis of the file system recovers evidence from IE while running in private mode whereas other browsers seem to maintain better user privacy. We analyse volatile memory and demonstrate how physical memory by means of dump files, hibernate and page files are the key areas where evidence from all browsers will still be recoverable despite their mode or location they run from

‘The language is disgusting and they refer to my disability’: the cyberharassment of disabled people

Disabled people face hostility and harassment in their sociocultural environment. The use of electronic-communications creates an online context that further reshape this discrimination. We explored the experiences of 19 disabled victims of cyberharassment. Five themes emerged from the study: disability and health consequences, family involvement, misrepresentation of self, perceived complexity, and lack of awareness and expertise. Cyberharassment incidents against disabled people were influenced by the pre-existing impairment, perceived hate-targeting, and perpetrators faking disability to get closer to victims online. Our findings highlight a growing issue requiring action and proper support

Effective methods to detect metamorphic malware: A systematic review

The succeeding code for metamorphic Malware is routinely rewritten to remain stealthy and undetected within infected environments. This characteristic is maintained by means of encryption and decryption methods, obfuscation through garbage code insertion, code transformation and registry modification which makes detection very challenging. The main objective of this study is to contribute an evidence-based narrative demonstrating the effectiveness of recent proposals. Sixteen primary studies were included in this analysis based on a pre-defined protocol. The majority of the reviewed detection methods used Opcode, Control Flow Graph (CFG) and API Call Graph. Key challenges facing the detection of metamorphic malware include code obfuscation, lack of dynamic capabilities to analyse code and application difficulty. Methods were further analysed on the basis of their approach, limitation, empirical evidence and key parameters such as dataset, Detection Rate (DR) and False Positive Rate (FPR)

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing casualties in the Arabian Peninsula, namely `floods'. Researching how we could achieve accurate classification suitable for short informal (colloquial) Arabic text (usually used on Twitter), which is highly inconsistent and received very little attention in this field. First, we provide a thorough technical demonstration consisting of the following stages: data collection (Twitter REST API), labelling, text pre-processing, data division and representation, and training models. This has been deployed using `R' in our experiment. We then evaluate classifiers' performance via four experiments conducted to measure the impact of different stemming techniques on the following classifiers SVM, J48, C5.0, NNET, NB and k-NN. The dataset used consisted of 1434 tweets in total. Our findings show that Support Vector Machine (SVM) was prominent in terms of accuracy (F1=0.933). Furthermore, applying McNemar's test shows that using SVM without stemming on Colloquial Arabic is significantly better than using stemming techniques

Cyberstalking: Investigating formal intervention and the role of Corporate Social Responsibility

Context Online harassment and stalking have been identified with growing accordance as anti-social behaviours, potentially with extreme consequences including indirect or direct physical injury, emotional distress and/or financial loss. Objective As part of our ongoing work to research and establish better understanding of cyberstalking, this study aims to investigate the role of Police, Mobile Operators, Internet Service Providers (ISPs) and owners/administrators of online platforms (e.g. websites, chatrooms) in terms of intervention in response to offences. We ask to what different authorities do people report incidents of cyberstalking? Do these authorities provide satisfactory responses or interventions? And how can this be improved? Furthermore, we discuss the role of Corporate Social Responsibility (CSR) to encourage the implementation of cyberstalking-aware schemes by service providers to support victims. In addition, CSR can be used as a means to measure the effects of externality factor in dictating the relationship between the impact of a given individuals’ privacy loss and strategic decisions on investment to security controls in an organisational context. Method A mixed method design has been used in this study. Data collection took place by means of an online survey made available for three years to record both qualitative and quantitative data. Overall, 305 participants responded from which 274 identified themselves as victims of online harassment. Result Our results suggest that most offences were communicated through private channels such as emails and/or mobile texts/calls. A significant number of victims did not report this to their service provider because they did not know they could. While Police were recognised as the first-point-of-contact in such cases, 41.6% of our sample did not contact the Police due to reasons such as fear of escalation, guilt/sympathy and self-blaming. Experiences from those who have reported offences to service providers demonstrate that no or very little support was offered. Overall, the majority of participants shared the view that third-party intervention is required on their behalf in order to mitigate risks associated with cyberstalking. An independent specialist anti-stalking organisation was a popular choice to act on their behalf followed by the Police and network providers. Conclusion Incidents are taking place on channels owned and controlled by large, cross-border international companies providing mobile services, webmail and social networking. The lack of support offered to victims in many cases of cyberstalking can be identified as Corporate Social Irresponsibility (CSI). We anticipate that awareness should be raised as regarding service providers’ liability and social responsibility towards adopting better strategies

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Cyber-disability hate cases in the UK: the documentation by the police and potential barriers to reporting

This is an accepted manuscript of an article due to be published by Springer in Advanced Sciences and Technologies for Security Applications on 21 May 2021. The accepted version of the publication may differ from the final published version.Disability hate crime is under-reported in the UK with perceived lim-ited support given to the victims. The use of online communication resulted in cyber-disability hate cases, recognised by the Police with the addition of an ‘online-flag’ in the documentation. However, the cases remain under-reported, with potential individual, societal and organisational barriers to reporting espe-cially during a pandemic. This paper aims to contextualise the reporting of cyber-disability hate cases, identify potential barriers, and provide recommendations to improve support to victims by the Police. The retrospective examination was car-ried out on disability-related cyber incidents documented by a police force in the UK for 19 months. Among 3,349 cyber-crimes, 23 cases were included. The anal-ysis covered descriptive statistics and qualitative document analysis (QDA). Only 0.7% of cyber incidents or 6.7% of cyber-hate incidents were disability re-lated. The age of victims ranged between 15 and 61 years, with a mean of 25.8 years. Most of the victims (78%) were from White ethnic background, and the majority were females (61.5%). Three overarching themes emerged from the qualitative data as influencers of reporting or documentation, these were: psy-chological impact, fear for safety, and the type of disability. Cyber-offences re-sulted in a serious impact on wellbeing, however, cases that included people with visible disabilities were more documented. Further awareness-raising targeting the police and public is needed to understand the impact of cyber-offences and recognise the different types of disabilities, which might encourage both report-ing and documentation