Pusat Pengajian Sains Komputer - Tesis

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A Machine Learning Classification Approach To Detect Tls-Based Malware Using Entropy-Based Flow Set Features
(2022-11)
Kinan
As internet encryption has grown to safeguard users’ privacy, malware has evolved to leverage encryption protocols such as Transport Layer Security (TLS) to conceal its hazardous connections. The difficulty and impracticality of decrypting TLS network traffic before it reaches the Intrusion Detection System (IDS) has driven numerous research studies to focus on anomaly-based malware detection without decryption employing various features and Machine Learning (ML) algorithms. Nonetheless, several of these studies used flow features with low feature importance value and poor capability to distinguish malicious flows, such as the number of packets sent and received in a flow or its duration. Furthermore, the outliers and frequency-based flow feature transformations (FTT) applied to mitigate the poor flow feature have several flaws. This thesis proposes a TLS-based malware detection (TLSMalDetect) approach based on ML classification to address flow feature utilization limitations in related work. TLSMalDetect includes periodicity-independent entropy-based flow set (EFS) features produced by an FFT technique. The efficiency of EFS features is assessed in two ways: (1) by comparing them to the relevant related work’s features of outliers and flow using four feature importance methods, and (2) by analyzing the classification performance in the scenarios with and without EFS features. This study also investigates TLSMalDetect detection performance using seven ML classification algorithms and identifies the one with the highest accuracy.
A Multi-objective Evolutionary Algorithm Based On Decomposition For Continuous Optimization Using A Step-function Technique
(2022-06)
Chuah, How Siang
Multi-objective optimization is an area of study which solves complex real-world problem that involves two or three objectives. Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) is one of the algorithms that utilize the concepts of decomposition and neighbourhood to solve multi-objective problems. One of the recent MOEA/D algorithms, i.e., Constant-distance based Neighbours for MOEA/D with Dynamic Weight Vector Adjustment (MOEA/D-AWACD), integrates the concept of a constant-distance neighbourhood and a dynamic weight vector design. This combination creates a flexible neighbourhood that can adapt to the weight vectors changes. However, MOEA/D-AWACD’s performance is dependent on a constant-distance parameter,
A Simulated Annealing-Based Hyper-Heuristic For The Flexible Job Shop Scheduling Problem
(2023-03)
Kelvin, Lim Ching Wei
Flexible job shop scheduling problem (FJSP) is a common optimisation problem in the industry. The use of parallel machines allows an operation to be executed on a machine assigned from a set of alternative machines, raising a combination of machine assignment and job sequencing sub-problems. A straightforward technique to solve the FJSP is to apply a pair of machine assignment rule (MAR) and job sequencing rule (JSR), i.e. a MAR-JSR pair. However, the performance of each MAR-JSR pair is problem-dependent. In addition, within an algorithm execution, the MAR-JSR pair performs differently at different problem states. Given a wide range of MAR-JSR permutations, selecting a suitable MAR-JSR pair for a FJSP becomes a challenge. Positive outcomes on the application of simulated annealing-based hyper-heuristic (SA-HH) in addressing similar scheduling problem has been reported in the literature. Hence, this research proposes the SA-HH to produce a heuristic scheme (HS) made up of MAR-JSR pairs to solve the FJSP. The proposed SA-HH also incorporates a set of problem state features to facilitate the application of MAR-JSR pairs in the HS according to the state of the FJSP. This research investigates two variants of SA-HH, i.e. SA-HH based on the HS with problem state features (SA-HHPSF) and without problem state features (SA-HHNO-PSF).
A Visual Approach For Requirement Traceability
(2022-12)
Madaki, Abdulkadir Ahmad
Requirement traceability is a significant method of tracing and identifying the life of requirement in forward and backward directions during software development lifecycle. It is used to support impact analysis, requirement change, maintenance, verification, and validation of a software system. Visualization is one of the most suitable visual representations of requirement traceability data as it has so many aspects that can be explored. It offers detail and visible demographic symbols to show the traceability of requirements artefacts relationships. However, displaying many traceability links effectively and efficiently is a big challenge, because a software system with large numbers of artefacts and traceability links quickly gives rise to scalability and visual clutter issues. Therefore, a visual framework is designed and implemented as a tool to visualize and manage the traceability of requirements artefacts relationships. The framework follows an advance graphical user interface guide for Visual Information-Seeking which focus on overview first, zoom and filter, then details-on-demand. The tool used visualization techniques as colour-coded symbols on a node-link diagram to present data. Users can traverse the graph for an impact analysis method to understand data and make decisions during the software development life cycle. The evaluation results show a positive respond in terms of usefulness and ease of use factors. The average score mean for usefulness are 4.33 (86%), whereas the average score mean for ease of use are 4.25 (85%). This results show that the framework is useful in tracing links between requirements artefacts, easy to use as is highly effective to improve user interaction.
An Efficient Distributed Slotted Multi-Hop Wireless Mac Protocol For Internet Of Things
(2021-11)
Mohammad Ali Sarvghadi
In Wireless Sensor Networks (WSNs), the Media Access Control (MAC) layer is responsible for coordinating Sensor Nodes (SNs) as their access to the media. Real time data flow needs precise scheduling for sensor nodes to access the medium in order to send the data on time. Time slotted transmission scheduling is needed to access the media without collision. To achieve this, sensor nodes need to be synchronized and know about each other’s notion of time. However, time synchronization requires message exchanges, which incur high overhead. The proposed MAC protocol, which consists of algorithms that minimizes collision and detects hidden terminals, eliminates the message overheads of global time synchronization
Analysis Of Feature Reduction Algorithms To Estimate Human Stress Conditions
(2022-09)
Arasu, Darshan Babu
Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone’s life. Thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Therefore, this study aimed to present analyse of the performance of feature classify when combining with feature selection algorithm to estimate human stress based on the facial feature of thermal imaging. Three hybrid classifiers, Support Vector Machine (SVM), Decision Tree (DT) and Logistic Regression (LR) combined with feature reduction analysis, Principal Component Analyse (PCA) and Analysis of Variance (ANOVA) was evaluated with 10-fold validation to compute classification accuracy. Four statistical features was extracted; mean, maximum, minimum and standard deviation of the gray scale value from six area regions of interest. Results showing that hybrid classifier DT-ANOVA achieves higher accuracy of 62% compared to others 90 combination classifiers. The findings demonstrated that DT-ANOVA performs well with a small dataset, while SVM and LR can improve the accuracy when fused with ANOVA for a big dataset. The findings also suggested that ANOVA can provides comparable performance as PCA.
Classification Of Cpg Island And Promoter Regions Using Rare K-Mer Motifs
(2015-07)
Mohamed Hashim,Ezzeddin Kamil
Empirical analysis on DNA k-mers is proven to be an effective means to discover functional elements in the human genomes. Among the empirical works, “rare k-mer" (RKM) is a very interesting subject to be studied due to their unique sequence properties. RKMs were referred as DNA k-mers (of k=7 to 11) that have a low frequency in k-mer frequency distributions of mammalian genomes, yet there are large variations of their mass at the lower k-mer spectra. Our first objective is to discover RKM motifs in the human genome; to identify potential RKM computational applications in biology; and to infer representations for the identified applications. In short, the first goal was achieved by using comparative strategy and several bioinformatic tools (of UCSC browsers, EpiGRAPH, and Galaxy) which correlated RKMs with several genomic features of CpG Islands (CGIs), promoter, 5’ Un- Translated Regions (5’UTRs), and open chromatin regions; and by using intrinsic string mining approaches which identified several unique RKM topological, compositional, and clustering properties in the correlated genomic features.
Communication Efficient Decentralized Collaborative Learning Of Heterogeneous Deep Learning Models Using Distillation And Incentive-Based Techniques
(2023-07)
Zahid, Iqbal
Smart devices, collectively, have very valuable and real-time data which can be used to train very efficient deep learning models for AI applications. However, due to the sensitive nature of this data, people are more concerned about the privacy of their data and not willing to share it. Therefore, there is a need to learn from this valuable data in a decentralized fashion by withholding data localized on these intended devices and efficiently performing necessary computation on these devices by exploiting their computational resources. Statistical heterogeneity and fully model heterogeneity are among the key challenges in applying the Decentralized Learning (DL) approaches in real scenarios. Typically, all existing DL techniques assume that all devices would have homogeneous model architecture. However, in real applications of DL, due to different computational resources and distinct business needs of devices, it is intuitive that they may have completely different model architectures. Very limited work has been performed to address fully model heterogeneity problem. In the same way, some work has been performed to address the statistical heterogeneity however mostly is hard to apply in real scenarios or is only for limited use cases.
Compute Language Interface: A Transparent Wrapper Library For Multi Cpu-Gpu
(2013-03)
Ooi, Keng Siang
The Graphics Processing Unit (GPU) processing capability is getting more powerful than before. Compute intensive and data parallelism applications are proven to perform better on the GPU than on the Central Processing Unit (CPU). However, available General-Purpose Computing on Graphics Processing Unit (GPGPU) programming frameworks which are available publicly are unable to reach beyond the single computer limitation to utilize multiple CPUs and GPUs at different computers in a distributed computing system easily. This study presents the Compute Language Interface (CLI) which is a wrapper library that enables the existing OpenCL applications access to all available CPUs and GPUs in a distributed computing system through Message Passing Interface (MPI) transparently. It is designed to improve the scalability of the OpenCL applications on a distributed computing system while maintaining the same set of application programming interface (API) in the original OpenCL library. The applications can access all available CPUs and GPUs in different computers in a distributed computing system as ifall the CPUs and GPUs are in the same computer.
Concept And Relation Extraction Framework For Ontology Learning
(2023-08)
Al-Aswadi, Fatima Nadeem Salem
Extracting valuable knowledge and representing it in a machine-understandable form is considered one of the main challenges of semantic web and knowledge engineering fields. The explosive growth of textual data is coupled with the increasing demand for ontologies. Ontology Learning (OL) from text is a process that aims to automatically or semi-automatically extract and represent the knowledge from text into the machine-understandable form. Ontology is a core scheme representing knowledge as a set of concepts and their relationships within a domain. Extracting the concepts and their relations is the backbone for an OL system. The existing OL systems have many limitations and drawbacks, such as not efficient for extracting relevant concepts especially, for large-length dataset; depending on a large amount of predefined patterns to extract relations, and extracting very few types of relations. In this thesis, a framework called Concept and Relation Extraction Framework (CREF) is proposed. It consists of four main stages: enhancing pre-processing method, developing methodology for the concept extraction task, proposing a new text representation approach for relations, and improving relation extraction method. The first stage involves proposing a new Concept Extraction stopwords (CE-stopwords) for scientific publications while the second stage involves introducing a new Domain Time Relevance (DTR) metric and proposing a Developed Concept Extraction Method based on DTR called (DTR-DCEM).
Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
(2023-01)
Ravichandran, Bhavani Devi
The spread of Covid-19 misinformation on social media had significant real-world consequences, raising fears among internet users since the pandemic has begun. Worldwide, researchers have shown an interest in developing deception classification methods to reduce the issue. This study aims to create an accurate model for the classification of Covid-19 misinformation in social media. This research has also conducted a systematic literature review to identify the most efficient method for classification with 35 papers. According to existing studies, the most efficient method for classification with the highest accuracy is the ANFIS and the DNN models. Thus, it was identified that the hybrid model of ANFIS-DNN shows the highest accuracy results. Therefore, the main goal of this study is to classify Covid-19 misinformation using an optimised hybrid model of ANFIS-DNN on social media based on the level of risk. A total of 8,000 Malaysian-based Tweets were extracted from Twitter based on topics related to Covid-19. The dataset is explored, cleaned, pre-processed, and the tweets were grouped into BoW model. Then, the proposed ANFIS-DNN is used to run the pre-processed dataset and the accuracy performance result shows 99%. Evaluation performance indexes such as confusion matrix, and accuracy are implemented in this research. The proposed model is then compared with ANFIS, DNN, Logistic Regression, SVM, Random Forest, and XGBoost. Furthermore, the accuracy is compared with other related works.
Data Integrity For Cloud Computing With Homomorphic Encryption
(2022-08)
Awadallah, Ruba N S
Cloud computing is a new computing model in which resources are provided as general utilities that users can lease through the Internet on-demand fashion. How- ever, this technology has numerous data security concerns. Asymmetric Homomorphic cryptosystem has been acknowledged as one of the potential solutions for achieving secure cloud computing since it can provide data privacy and confidentiality. Homo- morphic Encryption (HE) is a cryptosystem that allows cloud computing to operate computations on encrypted data. However, HE schemes are non-compliance to indis- tinguishability under adaptive chosen-ciphertext attack (IND-CCA2) because of their malleable nature. Moreover, the client (data owner) cannot prove if the data has been manipulated by the Cloud Service Provider (CSP) since CSP has absolute authority over the client’s data. The client is also unable to trace the processes that are being applied to the data once the data is outsourced. The client can also not verify the CSP’s reliability in applying the required operations on the required data. Therefore, implementing HE alone is not sufficient against various data integrity attacks. Two dif- ferent verifiable cloud computing schemes are being proposed in this work to address these questions. The first verifiable cloud computing scheme uses modular arithmetic to produce verified data to verify the CSP’s HE computations over a finite integer field. The performance of the proposed scheme varied based on the underlying cryp- tosystems used. However, based on the tested cryptosystems, the scheme has 1.5% storage overhead and a computational overhead that can be configured to work below 1%.
Enhanced Automated Framework For Cattle Tracking And Classification
(2022-09)
Williams, Bello Rotimi
Employing computer vision-based methods in monitoring individual cows has become what researchers are striving for. Computer vision-based methods could be used to monitor each individual cows. The accuracy of the existing methods and frameworks is below expectation in handling these tasks. Moreover, they can still be improved to achieve better and more accurate results. The goal of this research is to provide a framework for better cattle tracking and classification systems. An enhanced object tracking algorithm (PFtmM) that integrates enhanced particle filter algorithm (PFtm) with mean-shift tracker (M) is proposed and deployed as first step to address the problems arise due to occurrence of occlusion and non-linear movement of cow objects in video. The integration of particle filter with mean-shift tracker considers the following techniques: (1) temporary memory for keeping tracks of occluded cow objects; (2) supplementing each algorithm’s weakness by the strength of the other for tracking non-linear movement. Strength of particle filter (PF) is its non-linearity property which it uses to track object’s non-linear movement but, with high computational time and search range as its weakness. Temporary memory (tm) strength is its ability to track full occlusion with reduced computational time and search range. Mean-shift strength is its sensitivity to object’s movement and colour distribution by using similarity function but, with inability to track object’s non-linear movement and full occlusion as its weakness.
Enhanced Bluetooth Low Energy 5 Aodv-Based Mesh Communication Protocol With Multipath Support
(2023-04)
Muhammad Rizwan Ghori
Wireless Ad-Hoc Networks (WAHN) are growing more widespread due to improvements in Internet of Things (IoT) technologies. Several WAHN technologies, including ZigBee, Z-Wave, Threads, Bluetooth Low Energy (BLE) are available. Despite progress on IoT, network-based routing continues to be a critical problem that has to be tackled. In view of the above, this study focuses on a BLE-based mesh network.
Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
(2023-07)
Jimale, Ali Olow
While splitting datasets, researchers assume that training set is exchangeable with test set and expect good classification performance. This assumption is invalid due to subject variability due to age differences. Classification models trained on activity data from one particular age group such as adults cannot generalize to activity data collected from a different age group such as elderly. Subject variability in the context of age is a valid problem that degrades the performance of activity recognition, but remains unexplored. Existing studies that investigated subject variability in activity recognition overlooked this problem and only focused on contextual subject variability, and intra-subject variability. This study investigates the effects of subject variability on the performance of sensor-based activity recognition. Elderly and adult datasets were used to evaluate the assessment techniques. Experiments on adult dataset only, experiments on elderly dataset only, and experiments on adult (as training) and elderly (as test) datasets were conducted using machine learning and deep learning. The results show a significant performance drop in activity recognition on different subjects with different age groups. On average, the drop in recognition accuracy is 9.75% and 12% for machine learning and deep learning models respectively. Conditional Generative Adversarial Network (CGAN) is an ideal solution to address subject variability.
Enhanced Heterogeneous Stacked Ensemble Machine Learning Model For Detecting Nigerian Politically Motivated Cyberhate
(2023-03)
Sallau, Mullah Nanlir
Hate speech is a universal problem from time immemorial. The high adoption of social media (SM) has made it a problem of gigantic proportions during elections in Nigeria. The anonymity enjoyed by the users is the main reason for the high volume of cyber hate in Nigeria's social media space. Politicians usually circulate different politically motivated hate messages on social media during elections. Though, different artificial intelligence (AI) approaches such as machine learning models have been developed to address the problem with reasonable success. Nonetheless, the problem persists and leads to a high rate of cyberhate crime in Nigeria. The main problem is the lack of research to build models to address peculiarities in Nigeria. These problems made existing models incapacitated in Nigeria's cyberspace. To solve the identified research gaps from the vantage point of a machine learning researcher, the problem was modelled as a text classification task. To achieve the main objective, the study proposed to enhance a technique called the stacking ensemble method. The proposed method is called the heterogeneous stacked ensemble (HSE).
Enhanced Se-Resnet 101 For Food Image Segmentation
(2022-12)
Suhaila Farhan Ahmad Abuowaida
In recent years, deep learning has demonstrated its usefulness and capability in computer vision due to its high accuracy and acceptability. This thesis focuses on the enhanced instance segmentation method for multiple types of food and the improved food volume estimation method for better food calorie estimation.
Enhancing Svd-Based Image Watermarking Strategies Based On Digital Chaos
(2022-08)
Wafa’hamdan Suleiman Alshoura
A digital image is a universal medium that carries sensitive information and has proliferated in recent years. The watermarking scheme is a technique used for protecting digital images and other content such as audio, video, and text. Image watermarking schemes have the ability to embed the owner’s information into a host image in an imperceptible manner, and can be extracted later in the detection phase. Recently, the hybrid singular value decomposition (SVD)-based watermarking schemes in the frequency domain have received considerable attention. The interest is as a result of SVD having stability and robust properties which makes it resistant to different well-known attacks. However, existing hybrid SVD schemes do not meet some critical watermarking requirements such as successful trade-offs between robustness and imperceptibility, large capacity, and high security. Hence, they produce ineffective results which are not robust and are prone to a variety of attacks. This study aims to bridge the gap by developing enhanced hybrid SVD-based image watermarking schemes to fulfil the aforementioned watermarking requirements. In the proposed schemes, random numbers and new embedding strategies are leveraged upon to address these issues as well as making the proposed schemes flexible and easy to implement. This study proposes three new schemes that can be implemented on different image formats (gray and color image). The design elements and the novel constructions incorporated in the proposed schemes makes sure that they surpass the existing schemes.
Ethical Framework On Breast Self-Examination System
(2022-09)
Khana, Rajes
Breast Cancer is the leading cause of mortality for females in Indonesia. There is a need for women in Indonesia to have a breast self-examination system (BSE) where they can seek information and consult their cases with any physician; However, violation of patient confidentiality, lack of patient trust in physician conduct, and lack of patient control over their data pose a major problem in the use of BSE system. For this reason, there is a need to design a BSE system that can track self-exam data and allow communication between patient and physician. In developing a BSE system, ethical and trustworthiness values should exist as a part of the system’s ability. A survey was conducted to identify the ethical and trustworthiness values that can be applied to the BSE system. Therefore, this research aims to identify ethical values, determine the suitable trustworthiness factors, and propose an ethical framework that suits the development of the BSE system. The explanatory mixed-method carried quantitative survey with 772 respondents. A qualitative focus group discussion is conducted with 32 participants leading to the importance of an ethical framework being used in the healthcare system. The findings indicate that ethical values positively influence the BSE system with a pvalue < 0.001, trustworthiness positively influences the BSE system with a p-value < 0.001, and an ethical framework is essential to implement in the BSE system due to the coefficient of determination (R2) = 0.509.
Extension Of Information Systems Success Model For The Gamification E-Learning System
(2022-10)
Al-Muwassefi, Hala Najwan Sabeh
The existing literature depicts that graduates’ skills gap between industry expectations and academic preparation has become a trending phenomenon worldwide. Universities have been called to play a vital role to instil in these students the most sought skills in surviving this 21st Century. Hence, it signals the need to propose a new innovative system to enhance students’ skills and examine the net benefits of this system and the predictors of net benefits among university students. Accordingly, this research proposes the Login Career System, a gamification e-learning system, to improve students’ skills by integrating gamification features and certified e-learning courses. Besides, based on the integration of the updated DeLone & McLean Information Systems Success Model and the philosophy of expectancy theory, this study builds a theoretical model to govern the investigation of information quality, system quality, service quality, and collaboration quality as predictors of net benefits, use, and user satisfaction. Additionally, the model is built to investigate the use and user satisfaction as predictors of net benefits, whereas the use of the system is also examined as a predictor of user satisfaction. The research model was extended to include perceived future skills, perceived future personal characteristics, and perceived future labour market knowledge as the three outcome factors generated from the net benefits.

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