arXiv subjects

Md Zahidul Islam

Publications and source records attributed to Md Zahidul Islam.

At least 37 records · Page 2Linked to original sources

arXiv2023

Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics

Epilepsy is one of the most prevalent neurological conditions, where an epileptic seizure is a transient occurrence due to abnormal, excessive and synchronous activity in the brain. Electroencephalogram signals emanating from the brain may be captured, analysed and then play a significant role in detection and prediction of epileptic seizures. In this work we enhance upon a previous approach that relied on the differing properties of the wavelet transform. Here we apply the Maximum Overlap Discrete Wavelet Transform to both reduce signal \textit{noise} and use signal variance exhibited at differing inherent frequency levels to develop various metrics of connection between the electrodes placed upon the scalp. %The properties of both the noise reduced signal and the interconnected electrodes differ significantly during the different brain states. Using short duration epochs, to approximate close to real time monitoring, together with simple statistical parameters derived from the reconstructed noise reduced signals we initiate seizure detection. To further improve performance we utilise graph theoretic indicators from derived electrode connectivity. From there we build the attribute space. We utilise open-source software and publicly available data to highlight the superior Recall/Sensitivity performance of our approach, when compared to existing published methods.

q-bio.NC↗

arXiv2023

Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.

cs.CR↗

arXiv2023

A Semi-Automated Hybrid Schema Matching Framework for Vegetation Data Integration

Integrating disparate and distributed vegetation data is critical for consistent and informed national policy development and management. Australia's National Vegetation Information System (NVIS) under the Department of Climate Change, Energy, the Environment and Water (DCCEEW) is the only nationally consistent vegetation database and hierarchical typology of vegetation types in different locations. Currently, this database employs manual approaches for integrating disparate state and territory datasets which is labour intensive and can be prone to human errors. To cope with the ever-increasing need for up to date vegetation data derived from heterogeneous data sources, a Semi-Automated Hybrid Matcher (SAHM) is proposed in this paper. SAHM utilizes both schema level and instance level matching following a two-tier matching framework. A key novel technique in SAHM called Multivariate Statistical Matching is proposed for automated schema scoring which takes advantage of domain knowledge and correlations between attributes to enhance the matching. To verify the effectiveness of the proposed framework, the performance of the individual as well as combined components of SAHM have been evaluated. The empirical evaluation shows the effectiveness of the proposed framework which outperforms existing state of the art methods like Cupid, Coma, Similarity Flooding, Jaccard Leven Matcher, Distribution Based Matcher, and EmbDI. In particular, SAHM achieves between 88% and 100% accuracy with significantly better F1 scores in comparison with state-of-the-art techniques. SAHM is also shown to be several orders of magnitude more efficient than existing techniques.

cs.DB↗

arXiv2023

Enhancing Cluster Quality of Numerical Datasets with Domain Ontology

Ontology-based clustering has gained attention in recent years due to the potential benefits of ontology. Current ontology-based clustering approaches have mainly been applied to reduce the dimensionality of attributes in text document clustering. Reduction in dimensionality of attributes using ontology helps to produce high quality clusters for a dataset. However, ontology-based approaches in clustering numerical datasets have not been gained enough attention. Moreover, some literature mentions that ontology-based clustering can produce either high quality or low-quality clusters from a dataset. Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters. For every dataset, we produce three datasets using domain ontology. We then cluster these datasets using a genetic algorithm-based clustering technique called GenClust++. The clusters of each dataset are evaluated in terms of Sum of Squared-Error (SSE). We use six numerical datasets to evaluate the performance of our ontology-based approach. The experimental results of our approach indicate that cluster quality gradually improves from lower to the higher levels of a domain ontology.

cs.DB↗

arXiv2023

Combined Location Online Weather Data: Easy-to-use Targeted Weather Analysis for Agriculture

The continuing effects of climate change require farmers and growers to have greater understanding of how these changes affect crop production. However, while climatic data is generally available to help provide much of that understanding, it can often be in a form not easy to digest. The proposed Combined Location Online Weather Data (CLOWD) framework is an easy-to-use online platform for analysing recent and historical weather data of any location within Australia at the click of a map. CLOWD requires no programming skills and operates in any HTML5 web browser on PC and mobile devices. It enables comparison between current and previous growing seasons over a range of environmental parameters, and can create a plain-English PDF report for offline use, using natural language generation (NLG). This paper details the platform, the design decisions taken and outlines how farmers and growers can use CLOWD to better understand current growing conditions. Prototypes of CLOWD are now online for PCs and smartphones.

cs.SI↗

arXiv2023

A Brief Overview of Software-Defined Networking

The Internet is the driving force of the new digital world, which has created a revolution. With the concept of the Internet of Things (IoT), almost everything is being connected to the internet. However, with the traditional IP network system, it is computationally very complex and costly to manage and configure the network, where the data plane and the control plane are tightly coupled. In order to simplify the network management tasks, software-defined networking (SDN) has been proposed as a promising paradigm shift towards an externalized and logically centralized network control plane. SDN decouples the control plane and the data plane and provides programmability to configure the network. To address the overwhelming advancement of this new technology, a holistic overview of SDN is provided in this paper by describing different layers and their functionalities in SDN. The paper presents a simple but effective overview of SDN, which will pave the way for the readers to understand this new technology and contribute to this field.

cs.NI↗

arXiv2022

A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying k nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of fourteen state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

cs.LG↗

arXiv2022

How Do Organizations Seek Cyber Assurance? Investigations on the Adoption of the Common Criteria and Beyond

Cyber assurance, which is the ability to operate under the onslaught of cyber attacks and other unexpected events, is essential for organizations facing inundating security threats on a daily basis. Organizations usually employ multiple strategies to conduct risk management to achieve cyber assurance. Utilizing cybersecurity standards and certifications can provide guidance for vendors to design and manufacture secure Information and Communication Technology (ICT) products as well as provide a level of assurance of the security functionality of the products for consumers. Hence, employing security standards and certifications is an effective strategy for risk management and cyber assurance. In this work, we begin with investigating the adoption of cybersecurity standards and certifications by surveying 258 participants from organizations across various countries and sectors. Specifically, we identify adoption barriers of the Common Criteria through the designed questionnaire. Taking into account the seven identified adoption barriers, we show the recommendations for promoting cybersecurity standards and certifications. Moreover, beyond cybersecurity standards and certifications, we shed light on other risk management strategies devised by our participants, which provides directions on cybersecurity approaches for enhancing cyber assurance in organizations.

cs.CR↗

arXiv2022

STRIDE-based Cyber Security Threat Modeling for IoT-enabled Precision Agriculture Systems

The concept of traditional farming is changing rapidly with the introduction of smart technologies like the Internet of Things (IoT). Under the concept of smart agriculture, precision agriculture is gaining popularity to enable Decision Support System (DSS)-based farming management that utilizes widespread IoT sensors and wireless connectivity to enable automated detection and optimization of resources. Undoubtedly the success of the system would be impacted on crop productivity, where failure would impact severely. Like many other cyber-physical systems, one of the growing challenges to avoid system adversity is to ensure the system's security, privacy, and trust. But what are the vulnerabilities, threats, and security issues we should consider while deploying precision agriculture? This paper has conducted a holistic threat modeling on component levels of precision agriculture's standard infrastructure using popular threat intelligence tools STRIDE to identify common security issues. Our modeling identifies a noticing of fifty-eight potential security threats to consider. This presentation systematically presented them and advised general mitigation suggestions to support cyber security in precision agriculture.

cs.CR↗

arXiv2021

A Generalised Logical Layered Architecture for Blockchain Technology

Precision, validity, reliability, timeliness, availability, and granularity are the desired characteristics for data and information systems. However due to the desired trait of data mutability, information systems have inherently lacked the ability to enforce data integrity without governance. A resolution to this challenge has emerged in the shape of blockchain architecture, which ensures immutability of stored information, whilst remaining in an online state. Blockchain technology achieves this through the serial attachment of set-sized parcels of data called blocks. Links (liken to a chain) between these blocks are implemented using a cryptographic seal created using mathematical functions on the data inside the blocks. Practical implementations of blockchain vary by different components, concepts, and terminologies. Researchers proposed various architectural models using different layers to implement blockchain technologies. In this paper, we investigated those layered architectures for different use cases. We identified essential layers and components for a generalised blockchain architecture. We present a novel three-tiered storage model for the purpose of logically defining and categorising blockchain as a storage technology. We envision that this generalised model will be used as a guide when referencing and building any blockchain storage solution.

cs.CR↗

arXiv2021

EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining

Epilepsy is one of the most common and yet diverse set of chronic neurological disorders. This excessive or synchronous neuronal activity is termed seizure. Electroencephalogram signal processing plays a significant role in detection and prediction of epileptic seizures. In this paper we introduce an approach that relies upon the properties of wavelets for seizure detection. We utilise the Maximum Overlap Discrete Wavelet Transform which enables us to reduce signal noise Then from the variance exhibited in wavelet coefficients we develop connectivity and communication efficiency between the electrodes as these properties differ significantly during a seizure period in comparison to a non-seizure period. We use basic statistical parameters derived from the reconstructed noise reduced signal, electrode connectivity and the efficiency of information transfer to build the attribute space. We have utilised data that are publicly available to test our method that is found to be significantly better than some existing approaches.

eess.SP↗

arXiv2021

Signal Classification using Smooth Coefficients of Multiple wavelets

Classification of time series signals has become an important construct and has many practical applications. With existing classifiers we may be able to accurately classify signals, however that accuracy may decline if using a reduced number of attributes. Transforming the data then undertaking reduction in dimensionality may improve the quality of the data analysis, decrease time required for classification and simplify models. We propose an approach, which chooses suitable wavelets to transform the data, then combines the output from these transforms to construct a dataset to then apply ensemble classifiers to. We demonstrate this on different data sets, across different classifiers and use differing evaluation methods. Our experimental results demonstrate the effectiveness of the proposed technique, compared to the approaches that use either raw signal data or a single wavelet transform.

stat.ML↗

arXiv2021

Differentially Private Random Decision Forests using Smooth Sensitivity

We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries. To do so, we build an ensemble of random decision trees that avoids querying the private data except to find the majority class label in the leaf nodes. Rather than using a count query to return the class counts like the current state-of-the-art, we use the Exponential Mechanism to only output the class label itself. This drastically reduces the sensitivity of the query -- often by several orders of magnitude -- which in turn reduces the amount of noise that must be added to preserve privacy. Our improved sensitivity is achieved by using "smooth sensitivity", which takes into account the specific data used in the query rather than assuming the worst-case scenario. We also extend work done on the optimal depth of random decision trees to handle continuous features, not just discrete features. This, along with several other improvements, allows us to create a differentially private decision forest with substantially higher predictive power than the current state-of-the-art.

cs.CR↗

arXiv2021

Adaptive Decision Forest: An Incremental Machine Learning Framework

In this study, we present an incremental machine learning framework called Adaptive Decision Forest (ADF), which produces a decision forest to classify new records. Based on our two novel theorems, we introduce a new splitting strategy called iSAT, which allows ADF to classify new records even if they are associated with previously unseen classes. ADF is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches. We evaluate ADF on five publicly available natural data sets and one synthetic data set, and compare the performance of ADF against the performance of eight state-of-the-art techniques. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.

cs.LG↗

arXiv2020

Detecting Autism Spectrum Disorder using Machine Learning

Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of Toddler, Child, Adolescent and Adult. We determine the best performing classifier for these binary datasets using the evaluation metrics recall, precision, F-measures and classification errors. Our finding shows that Sequential minimal optimization (SMO) based Support Vector Machines (SVM) classifier outperforms all other benchmark machine learning algorithms in terms of accuracy during the detection of ASD cases and produces less classification errors compared to other algorithms. Also, we find that Relief Attributes algorithm is the best to identify the most significant attributes in ASD datasets.

cs.LG↗

arXiv2020

FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy

Random Forest remains one of Data Mining's most enduring ensemble algorithms, achieving well-documented levels of accuracy and processing speed, as well as regularly appearing in new research. However, with data mining now reaching the domain of hardware-constrained devices such as smartphones and Internet of Things (IoT) devices, there is continued need for further research into algorithm efficiency to deliver greater processing speed without sacrificing accuracy. Our proposed FastForest algorithm delivers an average 24% increase in processing speed compared with Random Forest whilst maintaining (and frequently exceeding) it on classification accuracy over tests involving 45 datasets. FastForest achieves this result through a combination of three optimising components - Subsample Aggregating ('Subbagging'), Logarithmic Split-Point Sampling and Dynamic Restricted Subspacing. Moreover, detailed testing of Subbagging sizes has found an optimal scalar delivering a positive mix of processing performance and accuracy.

cs.LG↗

arXiv2020

A Novel Incremental Clustering Technique with Concept Drift Detection

Data are being collected from various aspects of life. These data can often arrive in chunks/batches. Traditional static clustering algorithms are not suitable for dynamic datasets, i.e., when data arrive in streams of chunks/batches. If we apply a conventional clustering technique over the combined dataset, then every time a new batch of data comes, the process can be slow and wasteful. Moreover, it can be challenging to store the combined dataset in memory due to its ever-increasing size. As a result, various incremental clustering techniques have been proposed. These techniques need to efficiently update the current clustering result whenever a new batch arrives, to adapt the current clustering result/solution with the latest data. These techniques also need the ability to detect concept drifts when the clustering pattern of a new batch is significantly different from older batches. Sometimes, clustering patterns may drift temporarily in a single batch while the next batches do not exhibit the drift. Therefore, incremental clustering techniques need the ability to detect a temporary drift and sustained drift. In this paper, we propose an efficient incremental clustering algorithm called UIClust. It is designed to cluster streams of data chunks, even when there are temporary or sustained concept drifts. We evaluate the performance of UIClust by comparing it with a recently published, high-quality incremental clustering algorithm. We use real and synthetic datasets. We compare the results by using well-known clustering evaluation criteria: entropy, sum of squared errors (SSE), and execution time. Our results show that UIClust outperforms the existing technique in all our experiments.

cs.LG↗

arXiv2020

Tree Index: A New Cluster Evaluation Technique

We introduce a cluster evaluation technique called Tree Index. Our Tree Index algorithm aims at describing the structural information of the clustering rather than the quantitative format of cluster-quality indexes (where the representation power of clustering is some cumulative error similar to vector quantization). Our Tree Index is finding margins amongst clusters for easy learning without the complications of Minimum Description Length. Our Tree Index produces a decision tree from the clustered data set, using the cluster identifiers as labels. It combines the entropy of each leaf with their depth. Intuitively, a shorter tree with pure leaves generalizes the data well (the clusters are easy to learn because they are well separated). So, the labels are meaningful clusters. If the clustering algorithm does not separate well, trees learned from their results will be large and too detailed. We show that, on the clustering results (obtained by various techniques) on a brain dataset, Tree Index discriminates between reasonable and non-sensible clusters. We confirm the effectiveness of Tree Index through graphical visualizations. Tree Index evaluates the sensible solutions higher than the non-sensible solutions while existing cluster-quality indexes fail to do so.

cs.LG↗