Physical Sciences and Mathematics Commons^™

Multi-Aspect Rule-Based Ai: Methods, Taxonomy, Challenges And Directions Towards Automation, Intelligence And Transparent Cybersecurity Modeling For Critical Infrastructures, Iqbal H. Sarker, Helge Janicke, Mohamed A. Ferrag, Alsharif Abuadbba

Research outputs 2022 to 2026

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today's cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human …

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 …

Machine Learning-Enhanced All-Photovoltaic Blended Systems For Energy-Efficient Sustainable Buildings, Mohammad Nur-E-Alam, Kazi Z. Mostofa, Boon K. Yap, Mohammad K. Basher, Mohammad A. Islam, Mikhail Vasiliev, Manzoore E. M. Soudagar, Narottam Das, Tiong S. Kiong

Research outputs 2022 to 2026

The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up …

Scene Graph Generation: A Comprehensive Survey, Hongsheng Li, Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Xia Zhao, Syed A. A. Shah, Mohammed Bennamoun

Research outputs 2022 to 2026

Deep learning techniques have led to remarkable breakthroughs in the field of object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image or a video into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. In this paper, a comprehensive survey of recent achievements is provided. This survey attempts to connect and systematize the existing visual relationship detection …

Unleashing The Power Of Internet Of Things And Blockchain: A Comprehensive Analysis And Future Directions, Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Sandeep Jagtap, Mohammad Iranmanesh, Salem Alghamdi, Yaser Alhasawi, Yasanur Kayikci

Research outputs 2022 to 2026

As the fusion of the Internet of Things (IoT) and blockchain technology advances, it is increasingly shaping diverse fields. The potential of this convergence to fortify security, enhance privacy, and streamline operations has ignited considerable academic interest, resulting in an impressive body of literature. However, there is a noticeable scarcity of studies employing Latent Dirichlet Allocation (LDA) to dissect and categorize this field. This review paper endeavours to bridge this gap by meticulously analysing a dataset of 4455 journal articles drawn solely from the Scopus database, cantered around IoT and blockchain applications. Utilizing LDA, we have extracted 14 distinct topics …

Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia

Research outputs 2022 to 2026

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

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 …

Opportunities And Challenges Posed By Disruptive And Converging Information Technologies For Australia's Future Defence Capabilities: A Horizon Scan, Pi-Shen Seet, Anton Klarin, Janice Jones, Mike Johnstone, Helen Cripps, Jalleh Sharafizad, Violetta Wilk, David Suter, Tony Marceddo

Research outputs 2022 to 2026

Introduction: The research project's objective was to conduct a comprehensive horizon scan of Network Centric Warfare (NCW) technologies—specifically, Cyber, IoT/IoBT, AI, and Autonomous Systems. Recognised as pivotal force multipliers, these technologies are critical to reshaping the mission, design, structure, and operations of the Australian Defence Force (ADF), aligning with the Department of Defence (Defence)’s offset strategies and ensuring technological advantage, especially in the Indo-Pacific's competitive landscape.

Research process: Employing a two-pronged research 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 …

Unifying Context With Labeled Property Graph: A Pipeline-Based System For Comprehensive Text Representation In Nlp, Ali Hur, Naeem Janjua, Mohiuddin Ahmed

Research outputs 2022 to 2026

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Improved Image Recognition Via Synthetic Plants Using 3d Modelling With Stochastic Variations, Chris C. Napier, David M. Cook, Leisa Armstrong, Dean Diepeveen

Research outputs 2022 to 2026

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural …

Key Communication Technologies, Applications, Protocols And Future Guides For Iot-Assisted Smart Grid Systems: A Review, Md Ohirul Qays, Iftekhar Ahmad, Ahmed Abu-Siada, Md Liton Hossain, Farhana Yasmin

Research outputs 2022 to 2026

Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things …

On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu

Research outputs 2022 to 2026

Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results …

A Systematic Collection Of Medical Image Datasets For Deep Learning, Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, Basheer Bennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed A. A. Shah, Mohammed Bennamoun

Research outputs 2022 to 2026

The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to medical data. Thus, as comprehensively as possible, this article …

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, …

Attention-Based Human Age Estimation From Face Images To Enhance Public Security, Md. Ashiqur Rahman, Shuhena S. Aonty, Kaushik Deb, Iqbal H. Sarker

Research outputs 2022 to 2026

Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Survey: An Overview Of Lightweight Rfid Authentication Protocols Suitable For The Maritime Internet Of Things, Glen Mudra, Hui Cui, Michael N. Johnstone

Research outputs 2022 to 2026

The maritime sector employs the Internet of Things (IoT) to exploit many of its benefits to maintain a competitive advantage and keep up with the growing demands of the global economy. The maritime IoT (MIoT) not only inherits similar security threats as the general IoT, it also faces cyber threats that do not exist in the traditional IoT due to factors such as the support for long-distance communication and low-bandwidth connectivity. Therefore, the MIoT presents a significant concern for the sustainability and security of the maritime industry, as a successful cyber attack can be detrimental to national security and have …

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 …

The Internet Of Things (Iot) In Healthcare: Taking Stock And Moving Forward, Abderahman Rejeb, Karim Rejeb, Horst Treiblmaier, Andrea Appolloni, Salem Alghamdi, Yaser Alhasawi, Mohammad Iranmanesh

Research outputs 2022 to 2026

Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and a bibliometric analysis were performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the …

Dos/Ddos-Mqtt-Iot: A Dataset For Evaluating Intrusions In Iot Networks Using The Mqtt Protocol, Alaa Alatram, Leslie F. Sikos, Mike Johnstone, Patryk Szewczyk, James Jin Kang

Research outputs 2022 to 2026

Adversaries may exploit a range of vulnerabilities in Internet of Things (IoT) environments. These vulnerabilities are typically exploited to carry out attacks, such as denial-of-service (DoS) attacks, either against the IoT devices themselves, or using the devices to perform the attacks. These attacks are often successful due to the nature of the protocols used in the IoT. One popular protocol used for machine-to-machine IoT communications is the Message Queueing Telemetry Protocol (MQTT). Countermeasures for attacks against MQTT include testing defenses with existing datasets. However, there is a lack of real-world test datasets in this area. For this reason, this paper …

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 …

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 …

On Irs-Assisted Covert Communication With A Friendly Uav, Xiaobei Xu, Linzi Hu, Sha Wei, Yuwen Qian, Shihao Yan, Feng Shu, Jun Li

Research outputs 2022 to 2026

Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, …

Star-Ris-Uav-Aided Coordinated Multipoint Cellular System For Multi-User Networks, Baihua Shi, Yang Wang, Danqi Li, Wenlong Cai, Jinyong Lin, Shuo Zhang, Weiping Shi, Shihao Yan, Feng Shu

Research outputs 2022 to 2026

Different from conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit signals to the receiver. In this paper, to serve more ground users and increase deployment flexibility, we investigate an unmanned aerial vehicle (UAV) equipped with STAR-RIS (STAR-RIS-UAV)-aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV-aided coordinated multipoint (CoMP) cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficient matrices are optimized. Specifically, …

Joint Beamforming And Phase Shift Design For Hybrid Irs And Uav-Aided Directional Modulation Networks, Rongen Dong, Hangjia He, Feng Shu, Qi Zhang, Riqing Chen, Shihao Yan, Jiangzhou Wang

Research outputs 2022 to 2026

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme …

Two Rapid Power Iterative Doa Estimators For Uav Emitter Using Massive/Ultra-Massive Receive Array, Yiwen Chen, Qijuan Jie, Yiqiao Zhang, Feng Shu, Xichao Zhan, Shihao Yan, Wenlong Cai, Xuehui Wang, Zhongwen Sun, Peng Zhang, Peng Chen

Research outputs 2022 to 2026

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance …

Determinants Of Cloud Computing Integration And Its Impact On Sustainable Performance In Smes: An Empirical Investigation Using The Sem-Ann Approach, Mohammed A. Al-Sharafi, Mohammad Iranmanesh, Mostafa Al-Emran, Ahmed I. Alzahrani, Fadi Herzallah, Norziana Jamil

Research outputs 2022 to 2026

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs …

A Comprehensive Review On Machine Learning In Healthcare Industry: Classification, Restrictions, Opportunities And Challenges, Qi An, Saifur Rahman, Jingwen Zhou, James Jin Kang

Research outputs 2022 to 2026

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning …