Engineering Commons^™

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 …

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 …

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 …

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, …

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.

Physical Layer Authenticated Image Encryption For Iot Network Based On Biometric Chaotic Signature For Mpfrft Ofdm System, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek Hassan

Research outputs 2022 to 2026

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which …

A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung

Research outputs 2022 to 2026

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …

A Provable Secure And Efficient Authentication Framework For Smart Manufacturing Industry, Muhammad Hammad, Akhtar Badshah, Ghulam Abbas, Hisham Alasmary, Muhammad Waqas, Wasim A. Khan

Research outputs 2022 to 2026

Smart manufacturing is transforming the manufacturing industry by enhancing productivity and quality, driving growth in the global economy. The Internet of Things (IoT) has played a crucial role in realizing Industry 4.0, where machines can communicate and interact in real-time. Despite these advancements, security remains a major challenge in developing and deploying smart manufacturing. As cyber-attacks become more prevalent, researchers are making security a top priority. Although IoT and Industrial IoT (IIoT) are used to establish smart industries, these systems remain vulnerable to various types of attacks. To address these security issues, numerous authentication methods have been proposed. However, many …

Green Underwater Wireless Communications Using Hybrid Optical-Acoustic Technologies, Kazi Y. Islam, Iftekhar Ahmad, Daryoush Habibi, M. Ishtiaque A. Zahed, Joarder Kamruzzaman

Research outputs 2014 to 2021

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

Application Of 3d Delaunay Triangulation In Fingerprint Authentication System, Wencheng Yang, Ahmed Ibrahim, Junaid Chaudhry, Song Wang, Jiankun Hu, Craig Valli

Research outputs 2014 to 2021

Biometric security has found many applications in Internet of Things (IoT) security. Many mobile devices including smart phones have supplied fingerprint authentication function. However, the authentication performance in such restricted environment has been downgraded significantly. A number of methods based on Delaunay triangulation have been proposed for minutiae-based fingerprint matching, due to some favorable properties of the Delaunay triangulation under image distortion. However, all existing methods are based on 2D pattern, of which each unit, a Delaunay triangle, can only provide limited discrimination ability and could cause low matching performance. In this paper, we propose a 3D Delaunay triangulation based …

Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai

Research outputs 2014 to 2021

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods …

Influence Of Substrate Stage Temperature And Rotation Rate On The Magneto-Optical Quality Of Rf-Sputtered Bi2.1dy0.9fe3.9ga1.1o12 Garnet Thin Films, Mohammad E. Alam, Mikhail Vasiliev, Kamal Alameh

Research outputs 2014 to 2021

Highly bismuth-substituted iron garnet thin films are prepared on quartz substrates by using a radio frequency (RF) magnetron sputtering technique. We study the factors (process parameters associated with the RF magnetron sputter deposition technique) affecting the magneto-optical (MO) properties of ferrite garnet films of composition Bi2.1Dy0.9Fe3.9Ga1.1O12. All films show high MO response across the visible range of wavelengths after being annealed. In particular, the effects of substrate stage temperature and rotation rate on the various properties of films are studied. Experimental results reveal that the characteristics of garnet films of this type can be tuned and optimized for use in …

A Security Review Of Local Government Using Nist Csf: A Case Study, Ahmed Ibrahim, Craig Valli, Ian Mcateer, Junaid Chaudhry

Research outputs 2014 to 2021

Evaluating cyber security risk is a challenging task regardless of an organisation’s nature of business or size, however, an essential activity. This paper uses the National Institute of Standards and Technology (NIST) cyber security framework (CSF) to assess the cyber security posture of a local government organisation in Western Australia. Our approach enabled the quantification of risks for specific NIST CSF core functions and respective categories and allowed making recommendations to address the gaps discovered to attain the desired level of compliance. This has led the organisation to strategically target areas related to their people, processes, and technologies, thus mitigating …

Ransomware Behavioural Analysis On Windows Platforms, Nikolai Hampton, Zubair A. Baig, Sherali Zeadally

Research outputs 2014 to 2021

Ransomware infections have grown exponentially during the recent past to cause major disruption in operations across a range of industries including the government. Through this research, we present an analysis of 14 strains of ransomware that infect Windows platforms, and we do a comparison of Windows Application Programming Interface (API) calls made through ransomware processes with baselines of normal operating system behaviour. The study identifies and reports salient features of ransomware as referred through the frequencies of API calls

A Novel Privacy Preserving User Identification Approach For Network Traffic, Nathan Clarke, Fudong Li, Steven Furnell

Research outputs 2014 to 2021

The prevalence of the Internet and cloud-based applications, alongside the technological evolution of smartphones, tablets and smartwatches, has resulted in users relying upon network connectivity more than ever before. This results in an increasingly voluminous footprint with respect to the network traffic that is created as a consequence. For network forensic examiners, this traffic represents a vital source of independent evidence in an environment where anti-forensics is increasingly challenging the validity of computer-based forensics. Performing network forensics today largely focuses upon an analysis based upon the Internet Protocol (IP) address – as this is the only characteristic available. More typically, …

A Feature-Based Structural Measure: An Image Similarity Measure For Face Recognition, Noor A. Shnain, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called …

Detecting And Tracing Slow Attacks On Mobile Phone User Service, Brian Cusack, Zhuang Tian

Australian Digital Forensics Conference

The lower bandwidth of mobile devices has until recently filtered the range of attacks on the Internet. However, recent research shows that DOS and DDOS attacks, worms and viruses, and a whole range of social engineering attacks are impacting on broadband smartphone users. In our research we have developed a metric-based system to detect the traditional slow attacks that can be effective using limited resources, and then employed combinations of Internet trace back techniques to identify sources of attacks. Our research question asked: What defence mechanisms are effective? We critically evaluate the available literature to appraise the current state of …

The Proceedings Of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia, Craig Valli

Australian Digital Forensics Conference

Conference Foreword

This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, …

Optical Fiber Sensors In Physical Intrusion Detection Systems: A Review, Gary Andrew Allwood, Graham Wild, Steven Hinkley

Research outputs 2014 to 2021

Fiber optic sensors have become a mainstream sensing technology within a large array of applications due to their inherent benefits. They are now used significantly in structural health monitoring, and are an essential solution for monitoring harsh environments. Since their first development over 30 years ago, they have also found promise in security applications. This paper reviews all of the optical fiber-based techniques used in physical intrusion detection systems. It details the different approaches used for sensing, interrogation, and networking, by research groups, attempting to secure both commercial and residential premises from physical security breaches. The advantages and the disadvantages …

A Forecasting Tool For Predicting Australia's Domestic Airline Passenger Demand Using A Genetic Algorithm, Panarat Srisaeng, Glenn Baxter, Steven Richardson, Graham Wild

Research outputs 2014 to 2021

This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia’s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown …

Cyber Blackbox For Collecting Network Evidence, Jooyoung Lee, Sunoh Choi, Yangseo Choi, Jonghyun Kim, Ikkyun Kim, Youngseok Lee

Australian Digital Forensics Conference

In recent years, the hottest topics in the security field are related to the advanced and persistent attacks. As an approach to solve this problem, we propose a cyber blackbox which collects and preserves network traffic on a virtual volume based WORM device, called EvidenceLock to ensure data integrity for security and forensic analysis. As a strategy to retain traffic for long enough periods, we introduce a deduplication method. Also this paper includes a study on the network evidence which is collected and preserved for analyzing the cause of cyber incident. Then, a method is proposed to suggest a starting …

Forensic Analysis Of A Sony Playstation 4: A First Look, Matthew Davies, Huw Read, Konstantinos Xynos, Iain Sutherland

Research outputs 2014 to 2021

The primary function of a games console is that of an entertainment system. However the latest iteration of these consoles has added a number of new interactive features that may prove of value to the digital investigator. This paper highlights the value of these consoles, in particular Sony's latest version of their PlayStation. This console provides a number of features including web browsing, downloading of material and chat functionality; all communication features that will be of interest to forensic investigators. In this paper we undertake an initial investigation of the PlayStation 4 games console. This paper identifies potential information sources …

The Zombies Strike Back: Towards Client-Side Beef Detection, Maxim Chernyshev, Peter Hannay

Australian Digital Forensics Conference

A web browser is an application that comes bundled with every consumer operating system, including both desktop and mobile platforms. A modern web browser is complex software that has access to system-level features, includes various plugins and requires the availability of an Internet connection. Like any multifaceted software products, web browsers are prone to numerous vulnerabilities. Exploitation of these vulnerabilities can result in destructive consequences ranging from identity theft to network infrastructure damage. BeEF, the Browser Exploitation Framework, allows taking advantage of these vulnerabilities to launch a diverse range of readily available attacks from within the browser context. Existing defensive …

A User-Oriented Network Forensic Analyser: The Design Of A High-Level Protocol Analyser, D Joy, F Li, N L. Clarke, S M. Furnell

Australian Digital Forensics Conference

Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how …

A Forensic Overview Of The Lg Smart Tv, Iain Sutherland, Konstantino Xynos, Huw Read, Andy Jones, Tom Drange

Australian Digital Forensics Conference

The emerging Smart TV platform will likely replace traditional television sets over time as the entertainment and communication centrepiece in people’s homes. Given its expanded functionality and now, its online presence, there is a need to identify how they may become part of forensic investigations. The purpose of this paper is to introduce the area of Smart TVs and the potential forensic value these systems present in combination with their ever advancing functionality and capabilities. We provide an overview of Smart TV systems highlighting functionality and potential issues. We also take an initial look at two particular models, from the …

Locational Wireless And Social Media-Based Surveillance, Maxim Chernyshev

Australian Digital Forensics Conference

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly …

Forensic Examination And Analysis Of The Prefetch Files On The Banking Trojan Malware Incidents, Andri P. Heriyanto

Australian Digital Forensics Conference

Whenever a program runs within the operating system, there will be data or artefacts created on the system. This condition applies to the malicious software (malware). Although they intend to obscure their presence on the system with anti-forensic techniques, still they have to run on the victim’s system to acquire their objective. Modern malware creates a significant challenge to the digital forensic community since they are being designed to leave limited traces and misdirect the examiner. Therefore, every examiner should consider performing all the forensics approaches such as memory forensic, live-response and Windows file analysis in the related malware incidents …

A Forensically-Enabled Iaas Cloud Computing Architecture, Saad Alqahtany, Nathan Clarke, Steven Furnell, Christoph Reich

Australian Digital Forensics Conference

Current cloud architectures do not support digital forensic investigators, nor comply with today’s digital forensics procedures largely due to the dynamic nature of the cloud. Whilst much research has focused upon identifying the problems that are introduced with a cloud-based system, to date there is a significant lack of research on adapting current digital forensic tools and techniques to a cloud environment. Data acquisition is the first and most important process within digital forensics – to ensure data integrity and admissibility. However, access to data and the control of resources in the cloud is still very much provider-dependent and complicated …

Up-Dating Investigation Models For Smart Phone Procedures, Brian Cusack, Raymond Lutui

Australian Digital Forensics Conference

The convergence of services in Smart Technologies such as iPhones, Androids and multiple tablet work surfaces challenges the scope of any forensic investigation to include cloud environments, devices and service media. The analysis of current investigation guidelines suggests that each element in an investigation requires an independent procedure to assure the preservation of evidence. However we dispute this view and review the possibility of consolidating current investigation guidelines into a unified best practice guideline. This exploratory research proposes to fill a gap in digital forensic investigation knowledge for smart technologies used in business environments and to propose a better way …