Computer Sciences Commons^™

Infrequent Pattern Detection For Reliable Network Traffic Analysis Using Robust Evolutionary Computation, A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Al-Sakib K. Pathan

Research outputs 2014 to 2021

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have …

Digital Forensic Readiness Intelligence Crime Repository, Victor R. Kebande, Nickson M. Karie, Kim-Kwang R. Choo, Sadi Alawadi

Research outputs 2014 to 2021

It may not always be possible to conduct a digital (forensic) investigation post-event if there is no process in place to preserve potential digital evidence. This study posits the importance of digital forensic readiness, or forensic-by-design, and presents an approach that can be used to construct a Digital Forensic Readiness Intelligence Repository (DFRIR). Based on the concept of knowledge sharing, the authors leverage this premise to suggest an intelligence repository. Such a repository can be used to cross-reference potential digital evidence (PDE) sources that may help digital investigators during the process. This approach employs a technique of capturing PDE from …

Digital Forensic Readiness In Operational Cloud Leveraging Iso/Iec 27043 Guidelines On Security Monitoring, Sheunesu Makura, H. S. Venter, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Sadi Alawadi

Research outputs 2014 to 2021

An increase in the use of cloud computing technologies by organizations has led to cybercriminals targeting cloud environments to orchestrate malicious attacks. Conversely, this has led to the need for proactive approaches through the use of digital forensic readiness (DFR). Existing studies have attempted to develop proactive prototypes using diverse agent-based solutions that are capable of extracting a forensically sound potential digital evidence. As a way to address this limitation and further evaluate the degree of PDE relevance in an operational platform, this study sought to develop a prototype in an operational cloud environment to achieve DFR in the cloud. …

An Investigation Into The Efficacy Of Url Content Filtering Systems, Brett Ronald Turner

Theses: Doctorates and Masters

Content filters are used to restrict to restrict minors from accessing to online content deemed inappropriate. While much research and evaluation has been done on the efficiency of content filters, there is little in the way of empirical research as to their efficacy. The accessing of inappropriate material by minors, and the role content filtering systems can play in preventing the accessing of inappropriate material, is largely assumed with little or no evidence. This thesis investigates if a content filter implemented with the stated aim of restricting specific Internet content from high school students achieved the goal of stopping students …

Affordance Learning For Visual-Semantic Perception, Chau Nguyen Duc Minh

Theses: Doctorates and Masters

Affordance Learning is linked to the study of interactions between robots and objects, including how robots perceive objects by scene understanding. This area has been popular in the Psychology, which has recently come to influence Computer Vision. In this way, Computer Vision has borrowed the concept of affordance from Psychology in order to develop Visual-Semantic recognition systems, and to develop the capabilities of robots to interact with objects, in particular. However, existing systems of Affordance Learning are still limited to detecting and segmenting object affordances, which is called Affordance Segmentation. Further, these systems are not designed to develop specific abilities …

A Defensive Strategy For Detecting Targeted Adversarial Poisoning Attacks In Machine Learning Trained Malware Detection Models, Adrian Michael Wood

Theses: Doctorates and Masters

Machine learning is a subset of Artificial Intelligence which is utilised in a variety of different fields to increase productivity, reduce overheads, and simplify the work process through training machines to automatically perform a task. Machine learning has been implemented in many different fields such as medical science, information technology, finance, and cyber security. Machine learning algorithms build models which identify patterns within data, which when applied to new data, can map the input to an output with a high degree of accuracy. To build the machine learning model, a dataset comprised of appropriate examples is divided into training and …

Toward A Sustainable Cybersecurity Ecosystem, Shahrin Sadik, Mohiuddin Ahmed, Leslie F. Sikos, A.K.M. Najmul Islam

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in …

The K-Means Algorithm: A Comprehensive Survey And Performance Evaluation, Mohiuddin Ahmed, Raihan Seraj, Syed Mohammed Shamsul Islam

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of …

From The Tree Of Knowledge And The Golem Of Prague To Kosher Autonomous Cars: The Ethics Of Artificial Intelligence Through Jewish Eyes, Nachshon Goltz, John Zeleznikow, Tracey Dowdeswell

Research outputs 2014 to 2021

This article discusses the regulation of artificial intelligence from a Jewish perspective, with an emphasis on the regulation of machine learning and its application to autonomous vehicles and machine learning. Through the Biblical story of Adam and Eve as well as Golem legends from Jewish folklore, we derive several basic principles that underlie a Jewish perspective on the moral and legal personhood of robots and other artificially intelligent agents. We argue that religious ethics in general, and Jewish ethics in particular, show us that the dangers of granting moral personhood to robots and in particular to autonomous vehicles lie not …

Investigation Of Enhanced Double Weight Code In Point To Point Access Networks, Hesham A. Bakarman, Ali Z. Ghazi Zahid, M. H. Mezher, Al Aboud Wahed Al-Isawi, Feras N. Hasoon, Saad H. Al-Isawi, Hussein A. Rasool, Sahbudin Shaari, Maitham Al-Alyawy, S. T. Yousif, Jaber K. Taher, Ali H. Mezher, Hala Musawy, W. Y. Chong, R. Zakaria

Research outputs 2014 to 2021

© 2020 Published under licence by IOP Publishing Ltd. In this paper, an investigation and evaluation to enhanced double weight (EDW) code is performed, a new technique for code structuring and building using modified arithmetical model has been given for the code in place of employing previous technique based on Trial Inspections. Innovative design has been employed for the code into P2P networks using diverse weighted EDW code to be fitting into optical CDMA relevance applications. A new developed relation for EDW code is presented, the relation is based on studying and experimenting the effect of input transmission power with …

Denial Of Service Attack Detection Through Machine Learning For The Iot, Naeem Firdous Syed, Zubair Baig, Ahmed Ibrahim, Craig Valli

Research outputs 2014 to 2021

Sustained Internet of Things (IoT) deployment and functioning are heavily reliant on the use of effective data communication protocols. In the IoT landscape, the publish/subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is popular. Cyber security threats against the MQTT protocol are anticipated to increase at par with its increasing use by IoT manufacturers. In particular, IoT is vulnerable to protocol-based Application layer Denial of Service (DoS) attacks, which have been known to cause widespread service disruption in legacy systems. In this paper, we propose an Application layer DoS attack detection framework for the MQTT protocol and test the scheme on …

Local Binary Pattern Based Algorithms For The Discrimination And Detection Of Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le

Theses: Doctorates and Masters

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact …

A Vision-Based Machine Learning Method For Barrier Access Control Using Vehicle License Plate Authentication, Kh Tohidul Islam, Ram Gopal Raj, Syed Mohammed Shamsul Islam, Sudanthi Wijewickrema, Md Sazzad Hossain, Tayla Razmovski, Stephen O’Leary

Research outputs 2014 to 2021

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, …

Divergence Of Safety And Security, David J. Brooks, Michael Coole

Research outputs 2014 to 2021

© 2020, The Author(s). Safety and security have similar goals, to provide social wellness through risk control. Such similarity has led to views of professional convergence; however, the professions of safety and security are distinct. Distinction arises from variances in concept definition, risk drivers, body of knowledge, and professional practice. This chapter explored the professional synergies and tensions between safety and security professionals, using task-related bodies of knowledge. Findings suggest that safety and security only have commonalities at the overarching abstract level. Common knowledge does exist with categories of risk management and control; however, differences are explicit. In safety, risk …

Quantifiable Isovist And Graph-Based Measures For Automatic Evaluation Of Different Area Types In Virtual Terrain Generation, Andrew Pech, Chiou Peng Lam, Martin Masek

Research outputs 2014 to 2021

© 2013 IEEE. This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers' intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the …

Self-Supervised Learning To Detect Key Frames In Videos, Xiang Yan, Syed Zulqarnain Gilani, Mingtao Feng, Liang Zhang, Hanlin Qin, Ajmal Mian

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from different backgrounds to annotate key frames in videos which is not only expensive and time consuming but …

Performances Of The Lbp Based Algorithm Over Cnn Models For Detecting Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le, Selam Ahderom, Kamal Alameh

Research outputs 2014 to 2021

Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, …

A Comprehensive Analysis Of Smart Ship Systems And Underlying Cybersecurity Issues, Dennis Bothur

Theses : Honours

The maritime domain benefits greatly from advanced technology and ubiquitous connectivity. From “smart” sensors to “augmented reality”, the opportunities to save costs and improve safety are endless. The aim of this dissertation is to study the capabilities of smart ship systems in the context of Internet-of-Things and analyse the potential cybersecurity risks and challenges that smart technologies may introduce into this accelerating digital economy.

The first part of this work investigates the architecture of a “Smart Ship System” and the primary subsystems, including the integrated bridge, navigation and communication systems, networking, operational systems, and sensor networks. The mapping of the …

Intelligent Building Systems: Security And Facility Professionals’ Understanding Of System Threats,Vulnerabilities And Mitigation Practice, David J. Brooks, Michael Coole, Paul Haskell-Dowland

Research outputs 2014 to 2021

Intelligent Buildings or Building Automation and Control Systems (BACS) are becoming common in buildings, driven by the commercial need for functionality, sharing of information, reduced costs and sustainable buildings. The facility manager often has BACS responsibility; however, their focus is generally not on BACS security. Nevertheless, if a BACS-manifested threat is realised, the impact to a building can be significant, through denial, loss or manipulation of the building and its services, resulting in loss of information or occupancy. Therefore, this study garnered a descriptive understanding of security and facility professionals’ knowledge of BACS, including vulnerabilities and mitigation practices. Results indicate …

Provenance-Aware Knowledge Representation: A Survey Of Data Models And Contextualized Knowledge Graphs, Leslie F. Sikos, Dean Philp

Research outputs 2014 to 2021

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance …

Migrating From Partial Least Squares Discriminant Analysis To Artificial Neural Networks: A Comparison Of Functionally Equivalent Visualisation And Feature Contribution Tools Using Jupyter Notebooks, Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke

Research outputs 2014 to 2021

Introduction:

Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.

Objectives:

We hypothesise that …

Applying Mobile Augmented Reality (Ar) To Teach Interior Design Students In Layout Plans: Evaluation Of Learning Effectiveness Based On The Arcs Model Of Learning Motivation Theory, Yuh-Shihng Chang, Kuo-Jui Hu, Cheng-Wei Chiang, Artur Lugmayr

Research outputs 2014 to 2021

In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of …

Use Of Landsat Imagery To Map Spread Of The Invasive Alien Species Acacia Nilotica In Baluran National Park, Indonesia, Sutomo Sutomo, Eddie Van Etten, Rajif Iryadi

Research outputs 2014 to 2021

© 2020 Seameo Biotrop. In the late 1960s, Acacia nilotica was introduced to Baluran National Park to establish fire breaks which would prevent the spread of fire from Baluran Savanna to the adjacent teak forest. However, A. nilotica has spread rapidly and has threatened the existence of Baluran Savanna as it has caused an ecosystem transition from an open savanna to a closed canopy of A. nilotica in some areas. This study is one of the few that examines A. nilotica invasion in Baluran National Park through remote sensing. Land cover dynamics were quantified using a supervised classification approach on …

Trade-Off Assessments Between Reading Cost And Accuracy Measures For Digital Camera Monitoring Of Recreational Boating Effort, Ebenezer Afrifa-Yamoah, Stephen M. Taylor, Ute Mueller

Research outputs 2014 to 2021

Digital camera monitoring is increasingly being used to monitor recreational fisheries. The manual interpretation of video imagery can be costly and time consuming. In an a posteriori analysis, we investigated trade-offs between the reading cost and accuracy measures of estimates of boat retrievals obtained at various sampling proportions for low, moderate and high traffic boat ramps in Western Australia. Simple random sampling, systematic sampling and stratified sampling designs with proportional and weighted allocation were evaluated to assess trade-offs in terms of bias, accuracy, precision, coverage rate and cost in estimating the annual total number of powerboat retrievals in 10,000 jackknife …

Quantifying The Need For Supervised Machine Learning In Conducting Live Forensic Analysis Of Emergent Configurations (Eco) In Iot Environments, Victor R. Kebande, Richard A. Ikuesan, Nickson M. Karie, Sadi Alawadi, Kim-Kwang Raymond Choo, Arafat Al-Dhaqm

Research outputs 2014 to 2021

© 2020 The Author(s) Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper.

Ontology‐Driven Perspective Of Cfraas, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Hein S. Venter

Research outputs 2014 to 2021

A Cloud Forensic Readiness as a Service (CFRaaS) model allows an environment to preemptively accumulate relevant potential digital evidence (PDE) which may be needed during a post‐event response process. The benefit of applying a CFRaaS model in a cloud environment, is that, it is designed to prevent the modification/tampering of the cloud architectures or the infrastructure during the reactive process, which if it could, may end up having far‐reaching implications. The authors of this article present the reactive process as a very costly exercise when the infrastructure must be reprogrammed every time the process is conducted. This may hamper successful …

No Soldiers Left Behind: An Iot-Based Low-Power Military Mobile Health System Design, James Jin Kang, Wencheng Yang, Gordana Dermody, Mohammadreza Ghasemian, Sasan Adibi, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless …

Sam-Sos: A Stochastic Software Architecture Modeling And Verification Approach For Complex System-Of-Systems, Ahmad Mohsin, Naeem Khalid Janjua, Syed M. S. Islam, Muhammad Ali Babar

Research outputs 2014 to 2021

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any …

Interpreting Health Events In Big Data Using Qualitative Traditions, Roschelle L. Fritz, Gordana Dermody

Research outputs 2014 to 2021

© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. …

Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because …