Cybersecurity Domain Research Articles

ChatGPT: Technology Frontiers and Cybersecurity Challenges

This paper examines the evolution and application of OpenAI's advanced conversational AI, ChatGPT, particularly within the domain of cybersecurity. With an architecture built on the Transformer model, ChatGPT demonstrates significant capabilities in language understanding and generation. It leverages vast datasets, ranging from social media posts to technical documents, ensuring the model adapts to diverse fields and maintains compliance with privacy and security regulations. The paper explores ChatGPT's role in network security, highlighting its proficiency in threat detection, vulnerability assessment, and incident response, essential as regulations like GDPR and CCPA become more stringent. Furthermore, the study addresses potential security risks associated with AI, such as phishing and misinformation, and discusses mitigation strategies through advanced training techniques like adversarial training and multi-task learning. A novel variational autoencoder (VAE)-based method, T-VAE, is introduced, offering enhanced generalization capabilities across different tasks and scenarios. The findings suggest that while ChatGPT has made significant strides in cybersecurity applications, continuous improvements in model robustness and adaptability are necessary to mitigate emerging threats and adapt to evolving digital landscapes.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

The survey on the dual nature of xAI challenges in intrusion detection and their potential for AI innovation

In the rapidly evolving domain of cybersecurity, the imperative for intrusion detection systems is undeniable; yet, it is increasingly clear that to meet the ever-growing challenges posed by sophisticated threats, intrusion detection itself stands in need of the transformative capabilities offered by the explainable artificial intelligence (xAI). As this concept is still developing, it poses an array of challenges that need addressing. This paper discusses 25 of such challenges of varying research interest, encountered in the domain of xAI, identified in the course of a targeted study. While these challenges may appear as obstacles, they concurrently present as significant research opportunities. These analysed challenges encompass a wide spectrum of concerns spanning the intersection of xAI and cybersecurity. The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further research and innovation in the quest for transparent and interpretable artificial intelligence that humans are able to trust. In addition to this, by reframing these challenges as opportunities, this study seeks to inspire and guide researchers towards realizing the full potential of xAI in cybersecurity.

Current Status and Challenges and Future Trends of Deep Learning-Based Intrusion Detection Models.

With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats.

Adaptive Cybersecurity Neural Networks: An Evolutionary Approach for Enhanced Attack Detection and Classification

The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP’s weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. Its performance was compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, and HHO. The results demonstrated that the proposed trainer consistently outperformed the other algorithms, achieving the lowest Mean Square Error (MSE) and highest classification accuracy across all datasets. Notably, the trainer reached a classification rate of 99.5% on the Bot-IoT dataset and 98.8% on the CSE-CIC-IDS2018 dataset, underscoring its effectiveness in detecting and classifying diverse cyber threats.

Design Insights for Developing Deepfakes Awareness Games for Adolescents

Around a decade ago, significant advancements in the deep learning domain like Generative Adversarial Networks (GANs) started to facilitate the creation of new synthetical content. The development of deepfake algorithms gained momentum due to the increase in computing power and open-source available resources which facilitated broader access to advanced deep learning methods. While initially deepfakes were created by creative pursuits in various media and marketing campaigns, they started to be misused driven by malicious purposes in various social media manipulation campaigns. These campaigns imply spreading disinformation and misinformation for discrediting individuals and creating confusion in relation to specific topics and events and are meant to altering users’ beliefs and behaviours. Such facts raised important ethical and societal concerns. Although deepfakes still represent a recent research area, a large body of studies is dedicated to, on the one hand, generation, and detection of deepfakes, and on the other hand, to understanding their implications and consequences for society. At the same time, governmental and practitioner efforts are devoted to containing their use and impact on society by proposing various strategies and programs. Nevertheless, both academic and societal efforts are in an incipient stage and often adopt a generalist perspective. This represents a crucial point in relation to the fact that many users in various social media platforms like TikTok is represented by adolescents that are known to be unaware and more vulnerable to media content in general, and deepfakes content, in particular. This represents the knowledge gap that this research aims to tackle by synthesizing design insights as requirements and guidelines that can be used when developing and deploying deepfakes awareness games for producing and/or enhancing awareness of adolescents. Accordingly, a systematic literature review is conducted and merged with previous experience in this domain by adopting a transdisciplinary research approach that merges methods and techniques from the deepfakes, AI, social media, cyber security, and gamification domains.

ENTROPY BASED EVALUATION OF THE IMPACT OF EDUCATION ON ECONOMIC DEVELOPMENT

In contemporary economic landscapes, effective managerial decision-making requires serious justifications often facilitated by information technologies. The article proposes the application of the foundational principles from information theory and entropy to determine the correlation between education and economic development. This research employs entropy methodology to explore the correlations and implications of these multifaceted domains, drawing insights from a range of global indices. By analyzing data from indices such as the Human Development Index, we investigate the relationship between average years of education and entropy values, shedding light on the diversity and standardization of human capital systems across nations. Moreover, economic indexes, encapsulating factors such as economic freedom and innovation, provide critical insights into the vibrancy and resilience of national economies. Through the lens of the Global Innovation Index, we explore how countries with higher levels of economic freedom and innovation efficiency exhibit less diversity in economic approaches and innovation strategies, leading to a reduction in entropy values. By examining entropy values in conjunction with innovation indicators, such as research and development expenditure and patent applications, we uncover patterns and trends indicative of the diversity or standardization of innovation strategies across countries. Additionally, cybersecurity emerges as a critical dimension of national security and resilience, with implications for economic well-being and societal trust. Through the National Cybersecurity Index, we assess the level of diversity and uncertainty in cybersecurity landscapes, highlighting the importance of robust cybersecurity measures in safeguarding against emerging threats and reducing entropy values. Ultimately, this research contributes to the discourse on sustainable development and resilience in an interconnected world. By identifying specific challenges and opportunities within education, economy, innovation, and cybersecurity domains, it facilitates informed decision-making and strategic interventions to promote inclusive growth and societal well-being on a global scale.

The social contract theory meets cybersecurity: systematic literature review

Purpose This study aims to provide a comprehensive overview of social contract theory (SCT) utilization in cybersecurity literature, elucidating the current state of research, identifying major applications and themes and highlighting gaps, particularly in empirical studies, and the integration of emerging technologies. The study also maps the contractual parties and governance tools discussed in SCT and cyberspace interactions. Design/methodology/approach This study uses a systematic literature review to investigate the application of SCT within the cybersecurity domain. Using a mixed-methods approach that combines quantitative and qualitative content analysis with the Grounded Theory strategy, over 30,000 documents were initially screened. The final data set comprised 873 peer-reviewed papers from multiple databases. MAXQDA software facilitated coding and thematic analysis to identify key SCT applications, governance tools and research gaps. Findings The review revealed the following: emerging technologies such as artificial intelligence (AI) and blockchain are sparsely researched within the SCT-cyberspace intersection, yet they offer solutions to various SCT-related issues; empirical studies are underrepresented, with theoretical explorations dominating the discourse; there is a notable gap in integrating emerging technologies such as AI within SCT frameworks; governance tools discussed are varied, including economic incentives, regulatory measures and informational strategies. Originality/value This study synthesizes SCT applications in cybersecurity, highlighting the interdisciplinary nature and potential for richer theoretical integration. By systematically mapping the literature, it identifies crucial gaps and offers a foundation for future empirical and theoretical research. The findings emphasize the importance of considering traditional SCT themes and contemporary technological contexts, contributing to the development of more robust frameworks for cyberspace governance.

RAF-AG: Report analysis framework for attack path generation

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances.This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

Evaluating and validating the Serious Slow Game Jam methodology as a mechanism for co-designing serious games to improve understanding of cybersecurity for different demographics

We present an evaluation of a Serious Slow Game Jam (SSGJ) methodology as a mechanism for co-designing serious games in the application domain of cybersecurity, to evaluate how the SSGJ methodology contributed to improving the understanding of cybersecurity for different demographics. The aim of this study was to evaluate how the SSGJ contributed to improving the understanding of cybersecurity for young persons between the ages of 11 and 16 years old who had no formal training or education in cybersecurity, and to validate and compare these results to previous work where the same SSGJ methodology was used with a different target demographic (i.e.,M.Sc. students with no formal training or education in secure coding). To this end, we engaged 23 participants between the ages of 11 and 16 years old for 5 consecutive days over a one-week period, in a multidisciplinary SSGJ involving domain-specific, pedagogical, and game design knowledge, and encouraged engagement in-between scheduled events of the SSGJ. Findings show improved confidence of participants in their knowledge of cybersecurity, for both demographics, after undertaking the Serious Slow Game Jam (from 41.2% to 76.5% for young persons, and from 12.5% to 62.5% for M.Sc. students). Free-text answers specifically indicate an improved understanding of cybersecurity in general, and one specific security vulnerability, attack or defence for a quarter of young persons, and the trichotomy of security vulnerabilities, attacks, and defences for three quarters of the M.Sc. students. Also, confidence in knowledge of game design improved for both demographics (from 47.1% to 82.4% for young persons and from 12.5% to 75% for M.Sc. students). The SSGJ methodology also successfully engaged both demographics of participants in-between scheduled days. Finally, two serious games in the application domain of cybersecurity are presented that were co-designed during the SSGJ with participants and produced as an output of the SSGJs.

Operational Technology resilience in the 2023 draft delegated act on cybersecurity for the power sector—An EU policy process analysis

The EU’s 2020 Cybersecurity Strategy promotes cybersecurity as essential for building a resilient, green, and digital Europe. Cleaner energy sources such as wind and solar are more volatile and thus need digital integration with Industrial Control Systems (ICS) for grid balancing. However, the digitization and the properties of cyberspace provide the ability to coordinate disruptive cyberattacks against power grid infrastructures. Digital weapons may be launched against ICS to start multiple cascading outages with a keystroke, causing large-scale blackouts we have never seen before. To reduce risk, the EU’s Strategy describes three objectives for ICS: Secure-by-design, resilient, and timely patched. In the strategy, the European Commission suggests a ”network code,” i.e. a delegated act for the electric power sector, setting rules for cybersecurity in cross-border electricity flows. The draft delegated act of November 2023 presents security requirements for Information and Communication Technology (ICT) and Network and Information Systems (NIS). Although ICS systems are used directly to manage electricity flows, ICS is only mentioned in one of the delegated act’s recitals as a subcategory of ICT products. Suppose Information Technology (IT) rather than Operational Technology (OT) is the focus of the delegated act. In that case, policymakers may not fulfill the EU cybersecurity strategy’s ICS objectives, thus failing to improve the resilience of power grid infrastructures and cross-border electricity flows. This study is a policy process analysis, and its contribution is threefold. First, a literature review is conducted to understand the extent to which the delegated act covers OT. Second, a framework condition analysis is applied to understand why the delegated act lacks OT-specific security requirements. Third, the analysis is extended to understand whether OT is sufficiently covered to achieve the EU strategy’s ICS objectives. In conclusion, our analysis shows a strong intention to include OT-specific security in the preparatory work of the delegated act, but that a stronger position of the IT communities forced OT onto the sideline. Further, the study shows weak fulfillment of general secure-by-design principles and security patch management. These results indicate that OT coverage in the delegated act is not in line with the expectations of the EU’s cybersecurity strategy and the delegated act’s early preparatory work. Therefore, we have suggested three measures to increase OT resilience focus in the act: (a) Define the expressions NIS, ICT services, ICT processes, and ICT in general as umbrella terms that include OT, (b) The foreseen minimum and advanced cybersecurity controls should require OT-specific measures, including holistic secure-by-design principles and patch management covering all patching phases, (c) Develop an OT implementation guide for the delegated act. Our work can be used by policymakers to optimize cybersecurity policy processes and by researchers studying socio-technical gaps in the cybersecurity domain.

Domain-Agnostic Representation of Side-Channels.

Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human-computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established fields of advanced sensing and signal processing underlie each example, enabling the unification of these currently otherwise isolated domains. Identified themes are collating under a proposed domain-agnostic SCS framework. This SCS framework enables a formalised and systematic approach to studying, detecting, and exploiting of side channels both within and between domains. Opportunities exist for modelling SCS as data structures, allowing for computation irrespective of domain. Future methodologies can take such data structures to enable cross- and intra-domain transferability of extraction techniques, perform side-channel leakage detection, and discover new side channels within target systems.

Cybersecurity preparedness of small-to-medium businesses: A Western Australia study with broader implications

This study was prompted by the scarcity of focused quantitative research on the cybersecurity of SMBs. Our research aimed to understand the factors influencing SMBs' approach to cybersecurity, their level of threat awareness and the importance placed on cybersecurity. It also explored the extent to which NIST CSF practices are implemented by SMBs while also detecting and ranking the prevalent challenges faced by SMBs. Additionally, resources that SMBs turn to for help and guidance were also evaluated. While the survey-based study was on Western Australian SMBs, the results are of more general and wider interest. Our study found the lack of funds to be the biggest hindrance to cybersecurity, along with a lack of knowledge on where to start implementing good security practices. SMBs also lacked familiarity with relevant regulations and frameworks. The study highlights areas for improvement, such as access control mechanisms, individual user accounts, formalised policies and procedures, and dedicated budgets. SMBs heavily rely on Google search for cybersecurity information, emphasising the need for optimised search results from authoritative sources. IT service providers and informal networks also emerge as important sources of cybersecurity guidance, while local universities could assist SMBs but remain underutilised in this regard. Interestingly, factors such as organisational size, industry sector, and revenue level did not significantly impact SMBs' perception of vulnerability to cyber threats. However, further investigation is needed to evaluate the effectiveness of different IT service models for SMBs' cybersecurity needs. Overall, the research provides valuable insights into the specific gaps and challenges faced by SMBs in the cybersecurity domain, as well as their preferred methods of seeking and consuming cybersecurity assistance. The findings can guide the development of targeted strategies and policies to enhance the cybersecurity posture of SMBs.

Cultivating self-efficacy to empower professionals’ re-up skilling in cybersecurity

Purpose The accelerated digital transformation and the growing emphasis on privacy, safety and security present ongoing challenges for cybersecurity experts. Alongside these challenges, the multidisciplinary, everchanging and complex nature of the cybersecurity domain has further challenged the acquisition and retention of cybersecurity talent. Empowering reskilling and upskilling in cybersecurity necessitates efficacious educational endeavours which promote self-confidence and foster a growth mindset. The purpose of this paper is to highlight that cultivating self-efficacy in cybersecurity education can help promote competency development and effectively address the prominent skills gaps. This notion applies equally to both aspiring individuals pursuing a career in cybersecurity and professionals in the field who may wish to better articulate the skills they already possess, the skills they lack and newly surfacing skills that need to be developed. Design/methodology/approach The study discusses the imminent need for adopting a “skills-first” approach in cybersecurity and explores innovative pedagogies and professional frameworks that can inform and frame such an approach. Subsequently, a critical analysis of the importance of self-efficacy towards motivating and supporting upskilling in cybersecurity is performed. A case study is presented, expanding the authors’ previous work on cybersecurity professional development, to demonstrate the mediating role that self-efficacy can play in developing core cybersecurity competencies. The case study presents the design of a new cybersecurity curriculum in the context of postgraduate, synchronous distance cybersecurity education, and it is utilised as a basis to discuss how the proposed curriculum cultivates self-efficacy attitudes. Findings A skills-first approach is becoming the new norm in contemporary workplaces. This work highlights the importance of actively nurturing self-efficacy attitudes through innovative cybersecurity curricula that can be tailored to the learners’ needs, instigating a drive for learning and, ultimately, helping learners effectively upskilling by portraying a self-directed learning path and a professional growth mindset in cybersecurity. Originality/value The authors present the importance of cultivating self-efficacy in higher and lifelong education to foster reskilling and upskilling in cybersecurity. An innovative cybersecurity curriculum was constructed and delivered with a group of learners demonstrating how self-efficacy can be leveraged through interactive, reflective and self-assessment educational activities that enhanced motivation and self-awareness, curiosity, attention to detail and resilience – key skills for a successful career in cybersecurity.

A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture.

Over the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be employed for malicious activities with public safety threats. However, detecting drones instantly and efficiently is a very difficult task due to their tiny size and swift flights. This paper presents a novel drone detection method using deep convolutional learning and deep transfer learning. The proposed algorithm employs a new feature extraction network, which is added to the modified YOU ONLY LOOK ONCE version2 (YOLOv2) network. The feature extraction model uses bypass connections to learn features from the training sets and solves the "vanishing gradient" problem caused by the increasing depth of the network. The structure of YOLOv2 is modified by replacing the rectified linear unit (relu) with a leaky-relu activation function and adding an extra convolutional layer with a stride of 2 to improve the small object detection accuracy. Using leaky-relu solves the "dying relu" problem. The additional convolution layer with a stride of 2 reduces the spatial dimensions of the feature maps and helps the network to focus on larger contextual information while still preserving the ability to detect small objects. The model is trained with a custom dataset that contains various types of drones, airplanes, birds, and helicopters under various weather conditions. The proposed model demonstrates a notable performance, achieving an accuracy of 77% on the test images with only 5 million learnable parameters in contrast to the Darknet53 + YOLOv3 model, which exhibits a 54% accuracy on the same test set despite employing 62 million learnable parameters.

Cyber Security – Current and Future Trends

During recent years, many researchers and professionals have revealed the endangerment of wireless communication technologies and systems from various cyberattacks, these attacks cause detriment and harm not only to private enterprises but to the government organizations as well. The attackers endeavor new techniques to challenge the security frameworks, use powerful tools and tricks to break any sized keys, security of private and sensitive data is in the stale mark. There are many advancements being developed to mitigate these attacks. In this conjunction, this paper gives a complete account of survey and review of the various exiting advanced cyber security standards along with challenges faced by the cyber security domain. The new generation attacks are discussed and documented in detail and the advanced key management schemes are also depicted. The quantum cryptography is discussed with its merits and future scope of the same. Overall, the paper would be a kind of technical report to the new researchers to get acquainted with the recent advancements in Cyber security domain.

A Survey on the Applications of Semi-supervised Learning to Cyber-security

Machine Learning’s widespread application owes to its ability to develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised and unsupervised algorithms by leveraging limited labeled data alongside abundant unlabeled data. This article presents a comprehensive survey of SSL in cyber-security, focusing on countering diverse cybercrimes, particularly intrusion detection. Despite its potential, a notable research gap persists, with few recent studies comprehensively reviewing SSL’s application in cyber-security. This study examines state-of-the-art SSL techniques tailored for cyber-security to address this gap. Relevant methods are identified, and their effectiveness is evaluated to empower researchers and practitioners with insights to enhance cyber-security measures. This work sheds light on SSL’s potential in addressing data scarcity in cyber-security domains in addition to outlining new research directions to advance this crucial field. By bridging this research gap, this manuscript paves the way for enhanced cyber-threat detection and mitigation in an increasingly interconnected world.

Cyber Protection Applications of Quantum Computing: A Review

Quantum computing is a cutting-edge field of information technology that harnesses the principles of quantum mechanics to perform computations. It has major implications for the cyber security industry. Existing cyber protection applications are working well, but there are still challenges and vulnerabilities in computer networks. Sometimes data and privacy are also compromised. These complications lead to research questions asking what kind of cyber protection applications of quantum computing are there and what potential methods or techniques can be used for cyber protection? These questions will reveal how much power quantum computing has and to what extent it can outperform the conventional computing systems. This scoping review was conducted by considering 815 papers. It showed the possibilities that can be achieved if quantum technologies are implemented in cyber environments. This scoping review discusses various domains such as algorithms and applications, bioinformatics, cloud and edge computing, the organization of complex systems, application areas focused on security and threats, and the broader quantum computing ecosystem. In each of these areas, there is significant scope for quantum computing to be implemented and to revolutionize the working environment. Numerous quantum computing applications for cyber protection and a number of techniques to protect our data and privacy were identified. The results are not limited to network security but also include data security. This paper also discusses societal aspects, e.g., the applications of quantum computing in the social sciences. This scoping review discusses how to enhance the efficiency and security of quantum computing in various cyber security domains. Additionally, it encourages the reader to think about what kind of techniques and methods can be deployed to secure the cyber world.

Unpacking the Complex Socio-Technical Systems Assemblages in Cybersecurity

The ensuing digital transformation means that cybersecurity solutioning increasingly occurs in the context of complex intractable socio-technical systems comprising non-technical elements, including human, social, and societal factors. These evolving cybersecurity ecosystem dynamics, at the confluence of cyber-physical-social spaces, present several challenges to techno-centric cybersecurity solutions including for risk assessment, threat modelling, and incident analysis. This paper unpacks the complexity of the cybersecurity domain and illustrates the associated socio-technical systems assemblages through a case study and situational analysis of a cybersecurity incident. It then reviews socio-technical systems analysis approaches from the safety management domain and discusses the alignment with and relevance for cybersecurity. The utility of these approaches is demonstrated by applying the functional resonance analysis method to the said cybersecurity incident. The situational analysis surfaces the diverse set of factors, including human, non-human, cultural, economic, institutional, and global, that directly played a role in the unfolding of the incident, and which need to be considered in risk assessment and incident analysis. Further, analysing the incident through the functional resonance analysis method shows the functional dependencies and cascade of performance variability between the different elements in this situation, which goes beyond simple, root-cause, linear causality, and purely technical explanations. Overall, the paper explicates the need for cybersecurity risk assessment and incident analysis that is commensurate with the complexity of underlying socio-technical cyber systems.

Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning

In the domain of cybersecurity, available annotated data are often scarce, especially for Chinese cybersecurity datasets, often necessitating the manual construction of datasets. The scarcity of samples is one of the challenges in researching cybersecurity, especially for the “no-relation” class. Since the annotation process typically focuses only on known relation classes, there are usually no training samples for the “no-relation” class. This poses a zero-shot classification problem, where during the classification process, there is a tendency to classify into a class with a relationship. Zero-shot classification tasks are particularly challenging in this context. Moreover, most relation classification models currently need to traverse all relations to calculate the class with the highest probability. Therefore, the problem of “computational redundancy” is another challenge faced. Thus, how to accurately and efficiently acquire cyberspace knowledge from heterogeneous data sources and address the challenges such as sample scarcity, zero-shot recognition, and computational redundancy is the main focus of this chapter. To address these problems, this chapter designs a multi-relation extraction model based on ontology rule-enhanced prompt learning, which is a parameter-sharing-based multi-task model. By introducing prompt learning, which has shown significant effectiveness in the few-shot domain, this chapter designs prompt templates combining discrete and continuous tokens and uses rule injection in prompt learning to solve the difficulties in zero-shot recognition of “no-relation” and computational redundancy issues, achieving efficient and accurate multi-relation extraction. Specifically, by constructing sub-prompts to achieve an efficient combination of templates, a parameter-sharing structure is used to implement knowledge extraction step by step: The first step constructs entity prompt templates combining discrete and continuous tokens, identifying the classes of two entities based on prompt learning. The second step involves rule injection, identifying whether it belongs to the “no-relation” class based on the combination of sub-prompts; if there is no connection between the classes of two entities, it is classified as “no relation”; if a connection exists, the candidate relation set is filtered out. The third step uses the pre-trained model and vectors from the first step, utilizing prompt learning and rule judgment to determine the relation class from the candidate relation set. Finally, the effectiveness of our model is validated on the general datasets TACRED, ReTACRED, and the cybersecurity dataset constructed in this paper.