Information Security Commons^™

Enhancing Cybersecurity Through Autonomous Knowledge Graph Construction By Integrating Heterogeneous Data Sources, Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

Research outputs 2022 to 2026

Cybersecurity plays a critical role in today’s modern human society, and leveraging knowledge graphs can enhance cybersecurity and privacy in the cyberspace. By harnessing the heterogeneous and vast amount of information on potential attacks, organizations can improve their ability to proactively detect and mitigate any threat or damage to their online valuable resources. Integrating critical cyberattack information into a knowledge graph offers a significant boost to cybersecurity, safeguarding cyberspace from malicious activities. This information can be obtained from structured and unstructured data, with a particular focus on extracting valuable insights from unstructured text through natural language processing (NLP). By storing …

Machine Learning For Computer-Aided Diagnostics From Complex Medical Images, Afsah Saleem

Theses: Doctorates and Masters

Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorptiometry (DXA). Among these, lateral spine DXA scans, commonly used for osteoporosis screening, offer a cost-effective and low-radiation opportunity for opportunistic CVD risk …

Text-To-Text Generative Approach For Enhanced Complex Word Identification, Patrycja Śliwiak, Syed Afaq Ali Shah

Research outputs 2022 to 2026

This paper presents a novel approach for solving the Complex Word Identification (CWI) task using the text-to-text generative model. The CWI task involves identifying complex words in text, which is a challenging Natural Language Processing task. To our knowledge, it is a first attempt to address CWI problem into text-to-text context. In this work, we propose a new methodology that leverages the power of the Transformer model to evaluate complexity of words in binary and probabilistic settings. We also propose a novel CWI dataset, which consists of 62,200 phrases, both complex and simple. We train and fine-tune our proposed model …

Forward And Backward Private Searchable Encryption For Cloud-Assisted Industrial Iot, Tianqi Peng, Bei Gong, Shanshan Tu, Abdallah Namoun, Sami Alshmrany, Muhammad Waqas, Hisham Alasmary, Sheng Chen

Research outputs 2022 to 2026

In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE. However, most existing schemes either trade the server’s large storage overhead for forward privacy or trade efficiency/overhead for weak backward privacy. These schemes cannot fully meet the security requirements of cloud-assisted IIoT systems. We propose a fast and firmly secure SSE scheme called …

An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme For Internet Of Things, Chong Guo, Bei Gong, Muhammad Waqas, Hisham Alasmary, Shanshan Tu, Sheng Chen

Research outputs 2022 to 2026

The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT’s security issues. However, existing CP-ABE schemes are inefficient and unsuitable for IoT devices with limited computing resources. To address this problem, this paper proposes an efficient pairing-free CP-ABE scheme for the IoT. The scheme is based on lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The proposed scheme …

Maritime Behaviour Anomaly Detection With Seasonal Context, Travis Rybicki, Martin Masek, Chiou Peng Lam

Research outputs 2022 to 2026

Monitoring maritime traffic has become an important task for ensuring the safety of vessels, as well as the goods, and persons that they may be transporting. An active area of research is the modelling of expected normal vessel behaviour so as to detect subsequent anomalies in new data. Anomalies indicate that a vessel is not behaving in an expected manner and their detection can be flagged for further investigation to identify whether the vessel needs assistance or intervention. An important factor for some vessels in determining normal behaviour is seasonal context. However, current approaches typically do not incorporate seasonality into …

A Survey Of Advanced Border Gateway Protocol Attack Detection Techniques, Ben A. Scott, Michael N. Johnstone, Patryk Szewczyk

Research outputs 2022 to 2026

The Internet's default inter-domain routing system, the Border Gateway Protocol (BGP), remains insecure. Detection techniques are dominated by approaches that involve large numbers of features, parameters, domain-specific tuning, and training, often contributing to an unacceptable computational cost. Efforts to detect anomalous activity in the BGP have been almost exclusively focused on single observable monitoring points and Autonomous Systems (ASs). BGP attacks can exploit and evade these limitations. In this paper, we review and evaluate categories of BGP attacks based on their complexity. Previously identified next-generation BGP detection techniques remain incapable of detecting advanced attacks that exploit single observable detection approaches …

Resilient Tcp Variant Enabling Smooth Network Updates For Software-Defined Data Center Networks, Abdul Basit Dogar, Sami Ullah, Yiran Zhang, Hisham Alasmary, Muhammad Waqas, Sheng Chen

Research outputs 2022 to 2026

Network updates have become increasingly prevalent since the broad adoption of software-defined networks (SDNs) in data centers. Modern TCP designs, including cutting-edge TCP variants DCTCP, CUBIC, and BBR, however, are not resilient to network updates that provoke flow rerouting. In this paper, we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates, because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance deterioration. We look into the causes and propose a network update-friendly TCP (NUFTCP), which is an extension of …

Detecting Anomalies In Blockchain Transactions Using Machine Learning Classifiers And Explainability Analysis, Mohammad Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley …

Automated Sensor Node Malicious Activity Detection With Explainability Analysis, Md Zubair, Helge Janicke, Ahmad Mohsin, Leandros Maglaras, Iqbal H. Sarker

Research outputs 2022 to 2026

Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the …

Agriculture 4.0 And Beyond: Evaluating Cyber Threat Intelligence Sources And Techniques In Smart Farming Ecosystems, Hang T. Bui, Hamed Aboutorab, Arash Mahboubi, Yansong Gao, Nazatul H. Sultan, Aufeef Chauhan, Mohammad Z. Parvez, Michael Bewong, Rafiqul Islam, Zahid Islam, Seyit A. Camtepe, Praveen Gauravaram, Dineshkumar Singh, M. A. Babar, Shihao Yan

Research outputs 2022 to 2026

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information …

Examination Of Traditional Botnet Detection On Iot-Based Bots, Ashley Woodiss-Field, Michael N. Johnstone, Paul Haskell-Dowland

Research outputs 2022 to 2026

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were …

The Impact Of Domain Name Server (Dns) Over Hypertext Transfer Protocol Secure (Https) On Cyber Security: Limitations, Challenges, And Detection Techniques, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Muhammad Haris, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

The DNS over HTTPS (Hypertext Transfer Protocol Secure) (DoH) is a new technology that encrypts DNS traffic, enhancing the privacy and security of end-users. However, the adoption of DoH is still facing several research challenges, such as ensuring security, compatibility, standardization, performance, privacy, and increasing user awareness. DoH significantly impacts network security, including better end-user privacy and security, challenges for network security professionals, increasing usage of encrypted malware communication, and difficulty adapting DNS-based security measures. Therefore, it is important to understand the impact of DoH on network security and develop new privacy-preserving techniques to allow the analysis of DoH traffic …

Irs-Enabled Noma Communication Systems: A Network Architecture Primer With Future Trends And Challenges, Haleema Sadia, Ahmad Kamal Hassan, Ziaul Haq Abbas, Ghulam Abbas, Muhammad Waqas, Zhu Han

Research outputs 2022 to 2026

Non-Orthogonal Multiple Access (NOMA) has already proven to be an effective multiple access scheme for 5th Generation (5G) wireless networks. It provides improved performance in terms of system throughput, spectral efficiency, fairness, and energy efficiency (EE). However, in conventional NOMA networks, performance degradation still exists because of the stochastic behavior of wireless channels. To combat this challenge, the concept of Intelligent Reflecting Surface (IRS) has risen to prominence as a low-cost intelligent solution for Beyond 5G (B5G) networks. In this paper, a modeling primer based on the integration of these two cutting-edge technologies, i.e., IRS and NOMA, for B5G wireless …

Leveraging Finite-Precision Errors In Chaotic Systems For Enhanced Image Encryption, B. M. El-Den, Saad Aldosary, Haitham Khaled, Tarek M. Hassan, Walid Raslan

Research outputs 2022 to 2026

This research explores the application of chaotic systems in generating pseudo-random numbers for encryption protocols, offering a novel perspective on addressing the challenges posed by limited computer precision in cryptographic applications. Chaotic systems, while promising for encryption, often suffer from degradation in their chaotic properties when implemented on computers with finite precision. Previous studies have primarily aimed to mitigate this issue, with limited consideration of harnessing finite-precision errors as a potential source of randomness. In this study, we propose an innovative encryption method that leverages finite-precision errors within chaotic systems. The algorithm generates a keystream based on lower bound error …

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Towards Blockchain-Based Secure Bgp Routing, Challenges And Future Research Directions, Qiong Yang, Li Ma, Shanshan Tu, Sami Ullah, Muhammad Waqas, Hisham Alasmary

Research outputs 2022 to 2026

Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and traceability advantages of blockchain offer …

Achieving Covert Communication With A Probabilistic Jamming Strategy, Xun Chen, Fujun Gao, Min Qiu, Jia Zhang, Feng Shu, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and …

Secrecy Rate Maximization For Active Reconfigurable Intelligent Surface Assisted Mimo Systems, Bin Gao, Jingru Zhao, Shihao Yan, Shaozhang Xiao

Research outputs 2022 to 2026

Reconfigurable intelligent surface (RIS) is a promising technology for future 6G communications and has been used to enhance secrecy performance. However, the performance improvement is restricted by the 'double fading' effect of the reflection channel link. To address this issue, we introduce an active RIS design, where the reflecting elements of RIS not only adjust the phase shift but also amplify the reflected signal through the amplifier integrated into its elements. To obtain a satisfactory solution to the non-convex problem resulting from this design, the penalty dual decomposition based alternating gradient projection (PDDAPG) method is proposed. We compare the proposed …

Genai In Rule-Based Systems For Iomt Security: Testing And Evaluation, Kulsoom S. Bughio, David M. Cook, Syed Afaq A. Shah

Research outputs 2022 to 2026

Generative AI (GenAI) represents a significant advancement in artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory …

National Cyber Security Licence Consultation Report: Stakeholder Consultation Workshops Consolidated Feedback Held Oct – Nov 2023, Nicola F. Johnson, Leslie F. Sikos, Ahmed Ibrahim, Marnie Mckee

Research outputs 2022 to 2026

The consultation workshops invited stakeholders to join a roundtable environment to comment on and discuss the Consultation Paper, shared by Project Lead Associate Professor Nicola Johnson, Edith Cowan University (ECU) in October 2023. The purpose of the Consultation Paper was to identify whether stakeholders agreed that the national cyber security licence the Research Team had developed to date was a best solution towards addressing K-12 students’ cyber security education needs, which was found to not presently be sufficient (see Johnson et al., 2022). Eight workshops were conducted over a month-long period held October to November 2023. 250 invitations were sent …

Human-Empathy Accessibility Learning (Heal) Intervention Model Towards Critical Soft Skills Development For Career Readiness Among Computing Undergraduate Students, Jami Cotler, Eszter Kiss, Dmitry Burshteyn, Eben Afrifa-Yamoah

Research outputs 2022 to 2026

This pilot mixed methods study explores the impact of Human-Empathy Accessibility Learning (HEAL) interventions on soft skills development in undergraduate computing students, emphasizing their role in job readiness and perceived employability. HEAL interventions aimed to enhance accessibility awareness, focusing on motivation, empathy, and emotional intelligence. Participants were assigned to control and experiment groups, with qualitative findings showing empathetic growth in the experiment group. Quantitative results partially supported qualitative findings, indicating statistically significant changes across measures. Despite quantitative limitations, short-term empathy interventions showed potential benefits for job readiness. The study discusses implications of mixed findings and recommends future research directions.

A Systematic Review Of K-12 Cybersecurity Education Around The World, Ahmed Ibrahim, Marnie Mckee, Leslie F. Sikos, Nicola F. Johnson

Research outputs 2022 to 2026

This paper presents a systematic review of K-12 cybersecurity education literature from around the world. 24 academic papers dated from 2013-2023 were eligible for inclusion in the literature established within the research protocol. An additional 19 gray literature sources comprised the total. A range of recurring common topics deemed as aspects of cybersecurity behavior or practice were identified. A variety of cybersecurity competencies and skills are needed for K-12 students to apply their knowledge. As may be expected to be the case with interdisciplinary fields, studies are inherently unclear in the use of their terminology, and this is compounded in …

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

Developing A Novel Ontology For Cybersecurity In Internet Of Medical Things-Enabled Remote Patient Monitoring, Kulsoom S. Bughio, David M. Cook, Syed A. A. Shah

Research outputs 2022 to 2026

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. …

Enhancing Resilience In The Australian Security Vetting Process: Application Of The Psycho-Social Resources For Resilience Scale – Vetting (Prrs-V), Alan James Jnr Davies

Theses: Doctorates and Masters

The disclosure or leaking of classified information by a trusted insider is an ongoing challenge for the national security apparatus in Australia and globally (Auditor General, 2018; Scott, 2020). In an effort to ensure that those with access to sensitive information will not disclose it, the Australian Government has a security vetting process. The Australian security vetting process measures a person's integrity by conducting a 'whole of person' assessment against several suitability indicators. In 2015, the Australian Government added resilience as a suitability indicator to its prescribed guidance on personnel security vetting, the Personnel Security Eligibility and Suitability of Personnel …

Cyberattacks And Security Of Cloud Computing: A Complete Guideline, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, …

A Novel Authentication Method That Combines Honeytokens And Google Authenticator, Vassilis Papaspirou, Maria Papathanasaki, Leandros Maglaras, Ioanna Kantzavelou, Christos Douligeris, Mohamed A. Ferrag, Helge Janicke

Research outputs 2022 to 2026

Despite the rapid development of technology, computer systems still rely heavily on passwords for security, which can be problematic. Although multi-factor authentication has been introduced, it is not completely effective against more advanced attacks. To address this, this study proposes a new two-factor authentication method that uses honeytokens. Honeytokens and Google Authenticator are combined to create a stronger authentication process. The proposed approach aims to provide additional layers of security and protection to computer systems, increasing their overall security beyond what is currently provided by single-password or standard two-factor authentication methods. The key difference is that the proposed system resembles …

Authenticated Public Key Elliptic Curve Based On Deep Convolutional Neural Network For Cybersecurity Image Encryption Application, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek M. Hassan

Research outputs 2022 to 2026

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE …

Modulation Recognition Of Low-Snr Uav Radar Signals Based On Bispectral Slices And Ga-Bp Neural Network, Xuemin Liu, Yaoliang Song, Kuiyu Chen, Shihao Yan, Si Chen, Baihua Shi

Research outputs 2022 to 2026

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we …