arXiv subjects

Shujun Li

Publications and source records attributed to Shujun Li.

At least 55 records · Page 3Linked to original sources

arXiv2022

Cyber Security and Online Safety Education for Schools in the UK: Looking through the Lens of Twitter Data

In recent years, digital technologies have grown in many ways. As a result, many school-aged children have been exposed to the digital world a lot. Children are using more digital technologies, so schools need to teach kids more about cyber security and online safety. Because of this, there are now more school programmes and projects that teach students about cyber security and online safety and help them learn and improve their skills. Still, despite many programmes and projects, there is not much proof of how many schools have taken part and helped spread the word about them. This work shows how we can learn about the size and scope of cyber security and online safety education in schools in the UK, a country with a very active and advanced cyber security education profile, using nearly 200k public tweets from over 15k schools. By using simple techniques like descriptive statistics and visualisation as well as advanced natural language processing (NLP) techniques like sentiment analysis and topic modelling, we show some new findings and insights about how UK schools as a sector have been doing on Twitter with their cyber security and online safety education activities. Our work has led to a range of large-scale and real-world evidence that can help inform people and organisations interested in cyber security and teaching online safety in schools.

cs.CY↗

arXiv2022

Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter

Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert users` cyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in non-expert users` tweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert users` perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.

cs.CR↗

arXiv2022

A Survey of User Perspectives on Security and Privacy in a Home Networking Environment

The security and privacy of smart home systems, particularly from a home user's perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this paper shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holistic methods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flow between multiple home devices and home users, some less-studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users.

cs.HC↗

arXiv2022

Lightweight Transformer in Federated Setting for Human Activity Recognition

Human activity recognition (HAR) is a machine learning task with important applications in healthcare especially in the context of home care of patients and older adults. HAR is often based on data collected from smart sensors, particularly smart home IoT devices such as smartphones, wearables and other body sensors. Deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for HAR, both in centralized and federated settings. However, these techniques have certain limitations: RNNs cannot be easily parallelized, CNNs have the limitation of sequence length, and both are computationally expensive. Moreover, in home healthcare applications the centralized approach can raise serious privacy concerns since the sensors used by a HAR classifier collect a lot of highly personal and sensitive data about people in the home. In this paper, to address some of such challenges facing HAR, we propose a novel lightweight (one-patch) transformer, which can combine the advantages of RNNs and CNNs without their major limitations, and also TransFed, a more privacy-friendly, federated learning-based HAR classifier using our proposed lightweight transformer. We designed a testbed to construct a new HAR dataset from five recruited human participants, and used the new dataset to evaluate the performance of the proposed HAR classifier in both federated and centralized settings. Additionally, we use another public dataset to evaluate the performance of the proposed HAR classifier in centralized setting to compare it with existing HAR classifiers. The experimental results showed that our proposed new solution outperformed state-of-the-art HAR classifiers based on CNNs and RNNs, whiling being more computationally efficient.

cs.CV↗

arXiv2022

A Systematic Literature Review of the Tension between the GDPR and Public Blockchain Systems

The blockchain technology has been rapidly growing since Bitcoin was invented in 2008. The most common type of blockchain systems, public (permisionless) blockchain systems have some unique features that lead to a tension with European Union's General Data Protection Regulation (GDPR) and other similar data protection laws. In this paper, we report the results of a systematic literature review (SLR) on 114 research papers discussing and/or addressing such a tension. To be the best of our know, our SLR is the most comprehensive review of this topic, leading a more in-depth and broader analysis of related research work on this important topic. Our results revealed that three main types of issues: (i) difficulties in exercising data subjects' rights such as the `right to be forgotten' (RTBF) due to the immutable nature of public blockchains; (ii) difficulties in identifying roles and responsibilities in the public blockchain data processing ecosystem (particularly on the identification of data controllers and data processors); (iii) ambiguities regarding the application of the relevant law(s) due to the distributed nature of blockchains. Our work also led to a better understanding of solutions for improving the GDPR compliance of public blockchain systems. Our work can help inform not only blockchain researchers and developers, but also policy makers and law markers to consider how to reconcile the tension between public blockchain systems and data protection laws (the GDPR and beyond).

cs.CR↗

arXiv2022

Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term ``deepfake''. Based on both the research literature and resources in English and in Chinese, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including 1) different definitions, 2) commonly used performance metrics and standards, and 3) deepfake-related datasets, challenges, competitions and benchmarks. In addition, the paper also reports a meta-review of 12 selected deepfake-related survey papers published in 2020 and 2021, focusing not only on the mentioned aspects, but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of aspects covered, and the first one covering both the English and Chinese literature and sources.

cs.CV↗

arXiv2022

A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

cs.CL↗

arXiv2022

"You Just Assume It Is In There, I Guess": UK Families' Application And Knowledge Of Smart Home Cyber Security

The Internet of Things (IoT) is increasingly present in many family homes, yet it is unclear precisely how well families understand the cyber security threats and risks of using such devices, and how possible it is for them to educate themselves on these topics. Using a survey of 553 parents and interviews with 25 families in the UK, we find that families do not consider home IoT devices to be significantly different in terms of threats than more traditional home computers, and believe the major risks to be largely mitigated through consumer protection regulation. As a result, parents focus on teaching being careful with devices to prolong device life use, exposing their families to additional security risks and modeling incorrect security behaviors to their children. This is a risk for the present and also one for the future, as children are not taught about the IoT, and appropriate cyber security management of such devices, at school. We go on to suggest that steps must be taken by manufacturers and governments or appropriate trusted institutions to improve the cyber security knowledge and behaviors of both adults and children in relation to the use of home IoT devices.

cs.CR↗

arXiv2022

Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators

Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether these AI-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods; a key finding given the proposed role of AA in real world applications such as spam-detection and forensic investigation.

cs.CL↗

arXiv2022

Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI

Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable artificial intelligence (XAI) based deep learning framework in a federated setting for ECG-based healthcare applications. The federated setting is used to solve issues such as data availability and privacy concerns. Furthermore, the proposed framework setting effectively classifies arrhythmia's using an autoencoder and a classifier, both based on a convolutional neural network (CNN). Additionally, we propose an XAI-based module on top of the proposed classifier to explain the classification results, which help clinical practitioners make quick and reliable decisions. The proposed framework was trained and tested using the MIT-BIH Arrhythmia database. The classifier achieved accuracy up to 94% and 98% for arrhythmia detection using noisy and clean data, respectively, with five-fold cross-validation.

cs.LG↗

arXiv2021

It was hard to find the words: Using an Autoethnographic Diary Study to Understand the Difficulties of Smart Home Cyber Security Practices

This study considers how well an autoethnographic diary study helps as a method to explore why families might struggle in the application of strong and cohesive cyber security measures within the smart home. Combining two human-computer interaction (HCI) research methods - the relatively unstructured process of autoethnography and the more structured diary study - allowed the first author to reflect on the differences between researchers or experts, and everyday users. Having a physical set of structured diary prompts allowed for a period of 'thinking as writing', enabling reflection upon how having expert knowledge may or may not translate into useful knowledge when dealing with everyday life. This is particularly beneficial in the context of home cyber security use, where first-person narratives have not made up part of the research corpus to date, despite a consistent recognition that users struggle to apply strong cyber security methods in personal contexts. The framing of the autoethnographic diary study contributes a very simple, but extremely powerful, tool for anyone with more knowledge than the average user of any technology, enabling the expert to reflect upon how they themselves have fared when using, understanding and discussing the technology in daily life.

cs.HC↗

arXiv2021

When Googling it doesn't work: The challenge of finding security advice for smart home devices

As users increasingly introduce Internet-connected devices into their homes, having access to accurate and relevant cyber security information is a fundamental means of ensuring safe use. Given the paucity of information provided with many devices at the time of purchase, this paper engages in a critical study of the type of advice that home Internet of Things (IoT) or smart device users might be presented with on the Internet to inform their cyber security practices. We base our research on an analysis of 427 web pages from 234 organisations that present information on security threats and relevant cyber security advice. The results show that users searching online for information are subject to an enormous range of advice and news from various sources with differing levels of credibility and relevance. With no clear explanation of how a user may assess the threats as they are pertinent to them, it becomes difficult to understand which pieces of advice would be the most effective in their situation. Recommendations are made to improve the clarity, consistency and availability of guidance from recognised sources to improve user access and understanding.

cs.CR↗

arXiv2021

Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests

Recently, there had been little notable activity from the once prominent hacktivist group, Anonymous. The group, responsible for activist-based cyber attacks on major businesses and governments, appeared to have fragmented after key members were arrested in 2013. In response to the major Black Lives Matter (BLM) protests that occurred after the killing of George Floyd, however, reports indicated that the group was back. To examine this apparent resurgence, we conduct a large-scale study of Anonymous affiliates on Twitter. To this end, we first use machine learning to identify a significant network of more than 33,000 Anonymous accounts. Through topic modelling of tweets collected from these accounts, we find evidence of sustained interest in topics related to BLM. We then use sentiment analysis on tweets focused on these topics, finding evidence of a united approach amongst the group, with positive tweets typically being used to express support towards BLM, and negative tweets typically being used to criticize police actions. Finally, we examine the presence of automation in the network, identifying indications of bot-like behavior across the majority of Anonymous accounts. These findings show that whilst the group has seen a resurgence during the protests, bot activity may be responsible for exaggerating the extent of this resurgence.

cs.CY↗

arXiv2021

The Shadowy Lives of Emojis: An Analysis of a Hacktivist Collective's Use of Emojis on Twitter

Emojis have established themselves as a popular means of communication in online messaging. Despite the apparent ubiquity in these image-based tokens, however, interpretation and ambiguity may allow for unique uses of emojis to appear. In this paper, we present the first examination of emoji usage by hacktivist groups via a study of the Anonymous collective on Twitter. This research aims to identify whether Anonymous affiliates have evolved their own approach to using emojis. To do this, we compare a large dataset of Anonymous tweets to a baseline tweet dataset from randomly sampled Twitter users using computational and qualitative analysis to compare their emoji usage. We utilise Word2Vec language models to examine the semantic relationships between emojis, identifying clear distinctions in the emoji-emoji relationships of Anonymous users. We then explore how emojis are used as a means of conveying emotions, finding that despite little commonality in emoji-emoji semantic ties, Anonymous emoji usage displays similar patterns of emotional purpose to the emojis of baseline Twitter users. Finally, we explore the textual context in which these emojis occur, finding that although similarities exist between the emoji usage of our Anonymous and baseline Twitter datasets, Anonymous users appear to have adopted more specific interpretations of certain emojis. This includes the use of emojis as a means of expressing adoration and infatuation towards notable Anonymous affiliates. These findings indicate that emojis appear to retain a considerable degree of similarity within Anonymous accounts as compared to more typical Twitter users. However, their are signs that emoji usage in Anonymous accounts has evolved somewhat, gaining additional group-specific associations that reveal new insights into the behaviours of this unusual collective.

cs.CL↗

arXiv2020

Behind the Mask: A Computational Study of Anonymous' Presence on Twitter

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.

cs.SI↗

arXiv2019

From Data Disclosure to Privacy Nudges: A Privacy-aware and User-centric Personal Data Management Framework

Although there are privacy-enhancing tools designed to protect users' online privacy, it is surprising to see a lack of user-centric solutions allowing privacy control based on the joint assessment of privacy risks and benefits, due to data disclosure to multiple platforms. In this paper, we propose a conceptual framework to fill the gap: aiming at the user-centric privacy protection, we show the framework can not only assess privacy risks in using online services but also the added values earned from data disclosure. Through following a human-in-the-loop approach, it is expected the framework provides a personalized solution via preference learning, continuous privacy assessment, behavior monitoring and nudging. Finally, we describe a case study towards "leisure travelers" and several future areas to be studied in the ongoing project.

cs.CR↗

arXiv2019

Algorithm Selection for Image Quality Assessment

Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subjective quality ratings. Many such methods have been proposed, both for blind IQA in which no original reference image is available as well as for the full-reference case. We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large margin. In this contribution we address the research question whether established methods to learn such an oracle can improve blind IQA. We applied AutoFolio, a state-of-the-art system that trains an algorithm selector to choose a well-performing algorithm for a given instance. We also trained deep neural networks to predict the best method. Our results did not give a positive answer, algorithm selection did not yield a significant improvement over the single best method. Looking into the results in depth, we observed that the noise in images may have played a role in why our trained classifiers could not predict the oracle. This motivates the consideration of noisiness in IQA methods, a property that has so far not been observed and that opens up several interesting new research questions and applications.

cs.CV↗

arXiv2018

Dynamic Analysis of Digital Chaotic Maps via State-Mapping Networks

Chaotic dynamics is widely used to design pseudo-random number generators and for other applications such as secure communications and encryption. This paper aims to study the dynamics of discrete-time chaotic maps in the digital (i.e., finite-precision) domain. Differing from the traditional approaches treating a digital chaotic map as a black box with different explanations according to the test results of the output, the dynamical properties of such chaotic maps are first explored with a fixed-point arithmetic, using the Logistic map and the Tent map as two representative examples, from a new perspective with the corresponding state-mapping networks (SMNs). In an SMN, every possible value in the digital domain is considered as a node and the mapping relationship between any pair of nodes is a directed edge. The scale-free properties of the Logistic map's SMN are proved. The analytic results are further extended to the scenario of floating-point arithmetic and for other chaotic maps. Understanding the network structure of a chaotic map's SMN in digital computers can facilitate counteracting the undesirable degeneration of chaotic dynamics in finite-precision domains, helping also classify and improve the randomness of pseudo-random number sequences generated by iterating chaotic maps.

cs.CR↗