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Nalin Arachchilage

Publications and source records attributed to Nalin Arachchilage.

15 recordsLinked to original sources

SoK: Secure Software-Based Multi-Domain Data Segregation

Modern mission-critical coordination demands seamless communication across multiple domains. Traditionally, Voice Communication Systems (VCS) have relied on physically separated Red/Black architectures to ensure voice and data segregation. While these hardware-based methods provide strong security assurances and remain foundational in high-security contexts, they can introduce significant complexity and scalability challenges as mission parameters expand into highly dynamic, multi-domain integrations. As defence, emergency response, and critical infrastructure operations increasingly require interoperable, flexible, and cost-efficient communication environments, there is a growing need to understand whether software-based approaches can provide comparable assurance while supporting modern operational requirements. This SoK characterises a transition to software-based Multi-Domain Data Segregation (MDDS) by integrating systems security, networking, and cryptography. Its goal is to consolidate existing research, identify shared architectural patterns, and address the security challenges facing next-generation high-assurance software-based VCS architectures. By assessing software-defined and virtualized approaches, this SoK supports the development of scalable, high-assurance VCS architectures that facilitate secure real-time coordination across diverse operational domains. Specifically, this SoK examines the security implications of Software-Defined Networking, Network Slicing, Separation Kernels, and Cross-Domain Solutions while addressing challenges posed by quantum computing through Post-Quantum Cryptography (PQC). This SoK provides a comprehensive analysis of the shift from hardware isolation to software segregation, serving as a crucial foundation for researchers and industry stakeholders aiming to enhance secure and adaptable VCS infrastructures for mission-critical operations.

cs.CR

Confident yet Concerned: Inconsistencies in Computing Students' Attitudes on Cybersecurity

Today's young adults are most immersed in technology, leading in feelings of powerlessness in managing online privacy across many platforms, and particularly susceptible to phishing attacks. This raises questions about their general, wide-ranging attitudes towards and management of cybersecurity. How do young, tech-savvy adults approach cybersecurity? We seek a better understanding of their cybersecurity knowledge, attitudes and experiences, in particular in addressing deceptive online communications. We surveyed a group of `lead users': computing university students (n = 236). By combining thematic analysis of open-ended responses with quantitative data, we provide insights into their experiences and perceptions. While students demonstrate reasonable cybersecurity awareness, their cybersecurity experiences vary, and inconsistencies exist around their practices, perceptions of responsibility, and support structures. Findings also reveal four key thematic tensions: 1) Computing students are knowledgeable yet have persistent incorrect beliefs, 2) They learn more about keeping safe from sources outside the classroom, 3) They have limited assistance and have fallen victim to cybercrime, and 4) Many are confident, yet others are concerned about their own safety and responsibility. Through cluster analysis of attitudes, we identify two groups, with one feeling less prepared, less confident, yet expressing a desire to learn more. Established measures of intentions and objective knowledge were correlated to preparedness. Self-efficacy correlated to confidence and predicted cluster membership.

cs.CR

Security Concerns in Generative AI Coding Assistants: Insights from Online Discussions on GitHub Copilot

Generative Artificial Intelligence (GenAI) has become a central component of many development tools (e.g., GitHub Copilot) that support software practitioners across multiple programming tasks, including code completion, documentation, and bug detection. However, current research has identified significant limitations and open issues in GenAI, including reliability, non-determinism, bias, and copyright infringement. While prior work has primarily focused on assessing the technical performance of these technologies for code generation, less attention has been paid to emerging concerns of software developers, particularly in the security realm. OBJECTIVE: This work explores security concerns regarding the use of GenAI-based coding assistants by analyzing challenges voiced by developers and software enthusiasts in public online forums. METHOD: We retrieved posts, comments, and discussion threads addressing security issues in GitHub Copilot from three popular platforms, namely Stack Overflow, Reddit, and Hacker News. These discussions were clustered using BERTopic and then synthesized using thematic analysis to identify distinct categories of security concerns. RESULTS: Four major concern areas were identified, including potential data leakage, code licensing, adversarial attacks (e.g., prompt injection), and insecure code suggestions, underscoring critical reflections on the limitations and trade-offs of GenAI in software engineering. IMPLICATIONS: Our findings contribute to a broader understanding of how developers perceive and engage with GenAI-based coding assistants, while highlighting key areas for improving their built-in security features.

cs.SE

When Security Meets Usability: An Empirical Investigation of Post-Quantum Cryptography APIs

Advances in quantum computing increasingly threaten the security and privacy of data protected by current cryptosystems, particularly those relying on public-key cryptography. In response, the international cybersecurity community has prioritized the implementation of Post-Quantum Cryptography (PQC), a new cryptographic standard designed to resist quantum attacks while operating on classical computers. The National Institute of Standards and Technology (NIST) has already standardized several PQC algorithms and plans to deprecate classical asymmetric schemes, such as RSA and ECDSA, by 2035. Despite this urgency, PQC adoption remains slow, often due to limited developer expertise. Application Programming Interfaces (APIs) are intended to bridge this gap, yet prior research on classical security APIs demonstrates that poor usability of cryptographic APIs can lead developers to introduce vulnerabilities during implementation of the applications, a risk amplified by the novelty and complexity of PQC. To date, the usability of PQC APIs has not been systematically studied. This research presents an empirical evaluation of the usability of the PQC APIs, observing how developers interact with APIs and documentation during software development tasks. The study identifies cognitive factors that influence the developer's performance when working with PQC primitives with minimal onboarding. The findings highlight opportunities across the PQC ecosystem to improve developer-facing guidance, terminology alignment, and workflow examples to better support non-specialists.

cs.CR

Advancing Interdisciplinary Approaches to Online Safety Research

The growing prevalence of negative experiences in online spaces demands urgent attention from the human-computer interaction (HCI) community. However, research on online safety remains fragmented across different HCI subfields, with limited communication and collaboration between disciplines. This siloed approach risks creating ineffective responses, including design solutions that fail to meet the diverse needs of users, and policy efforts that overlook critical usability concerns. This workshop aims to foster interdisciplinary dialogue on online safety by bringing together researchers from within and beyond HCI - including but not limited to Social Computing, Digital Design, Internet Policy, Cybersecurity, Ethics, and Social Sciences. By uniting researchers, policymakers, industry practitioners, and community advocates we aim to identify shared challenges in online safety research, highlight gaps in current knowledge, and establish common research priorities. The workshop will support the development of interdisciplinary research plans and establish collaborative environments - both within and beyond Australia - to action them.

cs.HC

SoK: A Systematic Review of Malware Ontologies and Taxonomies and Implications for the Quantum Era

The threat of quantum malware is real and a growing security concern that will have catastrophic scientific and technological impacts, if not addressed early. If weaponised or exploited especially by the wrong hands, malware will undermine highly sophisticated critical systems supported by next-generation quantum architectures, for example, in defence, communications, energy, and space. This paper explores the fundamental nature and implications of quantum malware to enable the future development of appropriate mitigations and defences, thereby protecting critical infrastructure. By conducting a systematic literature review (SLR) that draws on knowledge frameworks such as ontologies and taxonomies to explore malware, this provides insights into how malicious behaviours can be translated into attacks on quantum technologies, thereby providing a lens to analyse the severity of malware against quantum technologies. This study employs the European Competency Framework for Quantum Technologies (CFQT) as a guide to map malware behaviour to several competency layers, creating a foundation in this emerging field.

cs.CR

GDPRShield: AI-Powered GDPR Support for Software Developers in Small and Medium-Sized Enterprises

With the rapid increase in privacy violations in modern software development, regulatory frameworks such as the General Data Protection Regulation (GDPR) have been established to enforce strict data protection practices. However, insufficient privacy awareness among SME software developers contributes to failure in GDPR compliance. For instance, a developer unfamiliar with data minimization may build a system that collects excessive data, violating GDPR and risking fines. One reason for this lack of awareness is that developers in SMEs often take on multidisciplinary roles (e.g., front-end, back-end, database management, and privacy compliance), which limits specialization in privacy. This lack of awareness may lead to poor privacy attitudes, ultimately hindering the development of a strong organizational privacy culture. However, SMEs that achieve GDPR compliance may gain competitive advantages, such as increased user trust and marketing value, compared to others that do not. Therefore, in this paper, we introduce a novel AI-powered framework called "GDPRShield," specifically designed to enhance the GDPR awareness of SME software developers and, through this, improve their privacy attitudes. Simultaneously, GDPRShield boosts developers' motivation to comply with GDPR from the early stages of software development. It leverages functional requirements written as user stories to provide comprehensive GDPR-based privacy descriptions tailored to each requirement. Alongside improving awareness, GDPRShield strengthens motivation by presenting real-world consequences of noncompliance, such as heavy fines, reputational damage, and loss of user trust, aligned with each requirement. This dual focus on awareness and motivation leads developers to engage with GDPRShield, improving their GDPR compliance and privacy attitudes, which will help SMEs build a stronger privacy culture over time.

cs.CR

A Novel Framework To Assess Cybersecurity Capability Maturity

In today's rapidly evolving digital landscape, organisations face escalating cyber threats that can disrupt operations, compromise sensitive data, and inflict financial and reputational harm. A key reason for this lies in the organisations' lack of a clear understanding of their cybersecurity capabilities, leading to ineffective defences. To address this gap, Cybersecurity Capability Maturity Models (CCMMs) provide a systematic approach to assessing and enhancing an organisation's cybersecurity posture by focusing on capability maturity rather than merely implementing controls. However, their limitations, such as rigid structures, one-size-fits-all approach, complexity, gaps in security scope (i.e., technological, organisational, and human aspects) and lack of quantitative metrics, hinder their effectiveness. It makes implementing CCMMs in varying contexts challenging and results in fragmented, incomprehensive assessments. Therefore, we propose a novel Cybersecurity Capability Maturity Framework that is holistic, flexible, and measurable to provide organisations with a more relevant and impactful assessment to enhance their cybersecurity posture.

cs.CR

How Are We Doing With Using AI-Based Programming Assistants For Privacy-Related Code Generation? The Developers' Experience

With generative AI becoming widespread, the existence of AI-based programming assistants for developers is no surprise. Developers increasingly use them for their work, including generating code to fulfil the data protection requirements (privacy) of the apps they build. We wanted to know if the reality is the same as expectations of AI-based programming assistants when trying to fulfil software privacy requirements, and the challenges developers face when using AI-based programming assistants and how these can be improved. To this end, we conducted a survey with 51 professional developers worldwide. We found that AI-based programming assistants need to be improved in order for developers to better trust them with generating code that ensures privacy. In this paper, we provide some recommendations including model and system-level improvements and some key further research directions to improve AI-based programming assistants for developing secure code.

cs.SE

The Good, the Bad, and the (Un)Usable: A Rapid Literature Review on Privacy as Code

Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and security engineering activities through threat modeling methods for scrutinizing flaws in system architectures. Moreover, several techniques for the automatic identification of vulnerabilities and the generation of secure code implementations have also been proposed in the current literature. Conversely, such as-code approaches seem under-investigated in the privacy domain, with little work elaborating on (i) the automatic detection of privacy properties in source code or (ii) the generation of privacy-friendly code. In this work, we seek to characterize the current research landscape of Privacy as Code (PaC) methods and tools by conducting a rapid literature review. Our results suggest that PaC research is in its infancy, especially regarding the performance evaluation and usability assessment of the existing approaches. Based on these findings, we outline and discuss prospective research directions concerning empirical studies with software practitioners, the curation of benchmark datasets, and the role of generative AI technologies.

cs.SE

Examining Attacks on Consensus and Incentive Systems in Proof-of-Work Blockchains: A Systematic Literature Review

Cryptocurrencies have gained popularity due to their transparency, security, and accessibility compared to traditional financial systems, with Bitcoin, introduced in 2009, leading the market. Bitcoin's security relies on blockchain technology - a decentralized ledger consisting of a consensus and an incentive mechanism. The consensus mechanism, Proof of Work (PoW), requires miners to solve difficult cryptographic puzzles to add new blocks, while the incentive mechanism rewards them with newly minted bitcoins. However, as Bitcoin's acceptance grows, it faces increasing threats from attacks targeting these mechanisms, such as selfish mining, double-spending, and block withholding. These attacks compromise security, efficiency, and reward distribution. Recent research shows that these attacks can be combined with each other or with either malicious strategies, such as network-layer attacks, or non-malicious strategies, like honest mining. These combinations lead to more sophisticated attacks, increasing the attacker's success rates and profitability. Therefore, understanding and evaluating these attacks is essential for developing effective countermeasures and ensuring long-term security. This paper begins by examining individual attacks executed in isolation and their profitability. It then explores how combining these attacks with each other or with other malicious and non-malicious strategies can enhance their overall effectiveness and profitability. The analysis further explores how the deployment of attacks such as selfish mining and block withholding by multiple competing mining pools against each other impacts their economic returns. Lastly, a set of design guidelines is provided, outlining areas future work should focus on to prevent or mitigate the identified threats.

cs.CR

Integrating PETs into Software Applications: A Game-Based Learning Approach

The absence of data protection measures in software applications leads to data breaches, threatening end-user privacy and causing instabilities in organisations that developed those software. Privacy Enhancing Technologies (PETs) emerge as promising safeguards against data breaches. PETs minimise threats to personal data while enabling software to extract valuable insights from them. However, software developers often lack the adequate knowledge and awareness to develop PETs integrated software. This issue is exacerbated by insufficient PETs related learning approaches customised for software developers. Therefore, we propose "PETs-101", a novel game-based learning framework that motivates developers to integrate PETs into software. By doing so, it aims to improve developers' privacy-preserving software development behaviour rather than simply delivering the learning content on PETs. In future, the proposed framework will be empirically investigated and used as a foundation for developing an educational gaming intervention that trains developers to put PETs into practice.

cs.CR

PrivacyCube: Data Physicalization for Enhancing Privacy Awareness in IoT

People are increasingly bringing Internet of Things (IoT) devices into their homes without understanding how their data is gathered, processed, and used. We describe PrivacyCube, a novel data physicalization designed to increase privacy awareness within smart home environments. PrivacyCube visualizes IoT data consumption by displaying privacy-related notices. PrivacyCube aims to assist smart home occupants to (i) understand their data privacy better and (ii) have conversations around data management practices of IoT devices used within their homes. Using PrivacyCube, households can learn and make informed privacy decisions collectively. To evaluate PrivacyCube, we used multiple research methods throughout the different stages of design. We first conducted a focus group study in two stages with six participants to compare PrivacyCube to text and state-of-the-art privacy policies. We then deployed PrivacyCube in a 14-day-long field study with eight households. Our results show that PrivacyCube helps home occupants comprehend IoT privacy better with significantly increased privacy awareness at p < .05 (p=0.00041, t= -5.57). Participants preferred PrivacyCube over text privacy policies because it was comprehensive and easier to use. PrivacyCube and Privacy Label, a state-of-the-art approach, both received positive reviews from participants, with PrivacyCube being preferred for its interactivity and ability to encourage conversations. PrivacyCube was also considered by home occupants as a piece of home furniture, encouraging them to socialize and discuss IoT privacy implications using this device.

cs.CR

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence

The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their own LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using our novel unified evaluation framework through the lenses of people, process, and technology, under the pillars of benchmark functionality and integrity. Our research uncovered significant limitations, including biases, difficulties in measuring genuine reasoning, adaptability, implementation inconsistencies, prompt engineering complexity, evaluator diversity, and the overlooking of cultural and ideological norms in one comprehensive assessment. Our discussions emphasized the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of Artificial Intelligence (AI) advancements, including advocating for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs' complex behaviors and potential risks. Our study highlighted the necessity for a paradigm shift in LLM evaluation methodologies, underlining the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems' integration into society.

cs.AI

PrivacyCube: A Tangible Device for Improving Privacy Awareness in IoT

Consumers increasingly bring IoT devices into their living spaces without understanding how their data is collected, processed, and used. We present PrivacyCube, a novel tangible device designed to explore the extent to which privacy awareness in smart homes can be elevated. PrivacyCube visualises IoT devices' data consumption displaying privacy-related notices. PrivacyCube aims at assisting families to (i) understand key privacy aspects better and (ii) have conversations around data management practices of IoT devices. Thus, families can learn and make informed privacy decisions collectively.

cs.CR