Computer Science and Information Technologies (E-Journal)
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149 research outputs found
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Securing DNS over HTTPS traffic: a real-time analysis tool
Full text linkDNS over HTTPS (DoH) is a developing protocol that uses encryption to secure domain name system (DNS) queries within hypertext transfer protocol secure (HTTPS) connections, thereby improving privacy and security while browsing the web. This study involved the development of a live tool that captures and analyzes DoH traffic in order to classify it as either benign or malicious. We employed machine learning (ML) algorithms such as K-nearest neighbors (K-NN), random forest (RF), decision tree (DT), deep neural network (DNN), and support vector machine (SVM) to categorize the data. All of the algorithms, namely KNN, RF, and DT, achieved exceptional performance, with F1 scores of 1.0 or above for both precision and recall. The SVM and DNN both achieved exceptionally high scores, with only slight differences in accuracy. This tool employs a voting mechanism to arrive at a definitive classification decision. By integrating with the Mallory tool, it becomes possible to locally resolve DNS, which in turn allows for more accurate simulation of DoH queries. The evaluation results clearly indicate outstanding performance, confirming the tool's effectiveness in analyzing DoH traffic for network security and threat detection purposes
Technology adoption model for smart urban farming-a proposed conceptual model
Full text linkTechnological advancements have made their way into the heart of human civilization across numerous fields, namely healthcare, logistics, and agriculture. Amidst the sprouting issues and challenges in the agriculture sector, particularly, the growing trend of integrating agriculture and technologies is roaring. The public and private sectors work hand in hand with regard to addressing these complex issues and challenges that arise, aiming for efficient and sustainable possible solutions. This study is a continuation of a previous systematic literature review; hence, the main objective is to deliver a proposed conceptual model for technology adoption specifically for smart urban farming. Innovation diffusion theory (IDT) is used as the main foundation of the proposed conceptual model, supplemented with additional factors drawn from other exisiting technology adoption models both the originals and extended versions. The outcome of the study is expected to reveal valuable insights into the components affecting the technology adoption model in smart urban farming, which will be further laid out upon in the upcoming study, offering a robust framework for future studies and applications in smart urban farming
Capabilities of cellebrite universal forensics extraction device in mobile device forensics
Full text linkThe powerful digital forensics tool cellebrite universal forensics extraction device (UFED) extracts and analyzes mobile device data, helping investigators solve criminal and cybersecurity cases. Advanced methods and algorithms allow Cellebrite UFED to recover data from erased or obscured devices. Cellebrite UFED can pull data from call logs, texts, emails, and social media, providing valuable evidence for investigations. The use of smartphones and tablets in personal and professional settings has spurred the development of mobile device forensics. The intuitive user interface speeds up data extraction and analysis, revealing crucial information. It can decrypt encrypted data, recover deleted files, and extract data from multiple devices. The sector's best data extraction functionality, Cellebrite UFED, helps forensic analysts gather crucial evidence for investigations. Legal and ethical considerations are crucial in mobile device forensics. Legal considerations include allowing access to data, protecting privacy, and adhering to chain of custody protocols. Ethics include transparency, defamation, and information exploitation protection. Using Cellebrite UFED, researchers can navigate complex data on mobile devices more efficiently and precisely. Artificial intelligence (AI) and machine learning (ML) algorithms may automate data extraction in future tools. Examiners must train, maintain, and establish clear protocols for using Cellebrite UFED in forensic investigations
Clustering of uninhabitable houses using the optimized apriori algorithm
Full text linkClustering is one of the roles in data mining which is very popularly used for data problems in solving everyday problems. Various algorithms and methods can support clustering such as Apriori. The Apriori algorithm is an algorithm that applies unsupervised learning in completing association and clustering tasks so that the Apriori algorithm is able to complete clustering analysis in Uninhabitable Houses and gain new knowledge about associations. Where the results show that the combination of 2 itemsets with a tendency value for Gas Stove fuel of 3 kg and the installed power meter for the attribute item criteria results in a minimum support value of 77% and a minimum confidence value of 87%. This proves that a priori is capable of clustering Uninhabitable Houses to help government work programs
Optimizing development and operations from the project success perspective using the analytic hierarchy process
Full text linkBy merging development and operation disciplines, the approach known as development and operations (DevOps) can significantly improve the efficiency and effectiveness of software development. Despite its potential benefits, successfully implementing DevOps within traditional project management frameworks presents significant challenges. This study explores the critical factors influencing the implementation of DevOps practices from the project management perspective, specifically focusing on software development projects in the Ministry of Finance. This study utilizes the analytic hierarchy process (AHP) to prioritize the critical elements of project success criteria and DevOps factors necessary for effective implementation. The findings indicate that stakeholder satisfaction, quality, and value creation are the primary criteria for project success. Moreover, knowledge and skills, collaboration and communication, and robust infrastructure are pivotal factors for facilitating DevOps within project management. The study provides actionable insights for organizations aiming to improve their project outcomes by incorporating DevOps and offers a systematic approach to decision-making using AHP. This study recognizes limitations due to its focus on specific contexts and emphasizes the need for future research in diverse organizational environments to validate and expand these findings
Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search
Full text linkFor Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly
The impact of usability in information technology projects
Full text linkAchieving success in information system and technology (IS/IT) projects is a complex and multifaceted endeavour that has proven difficult. The literature is replete with project failures, but identifying the critical success factors contributing to favourable outcomes remains challenging. The triad of Time-Cost-Quality is widely accepted as key to achieving project success. While time and cost can be quantified and measured, quality is a more complex construct that requires different metrics and measurement approaches. Utilizing the PRISMA Methodology, this study initiated a comprehensive search across literature databases and identified 142 relevant articles pertaining to the specified keywords. A subset of ten articles was deemed suitable for further examination through rigorous screening and eligibility assessments. Notably, a primary finding indicates that despite recognizing usability as a critical element, there is a tendency to neglect usability enhancements due to time and resource constraints. Regarding the influence of usability on project success, the active involvement of end-users emerges as a pivotal factor. Moreover, fostering the enhancement of Human-Computer Interaction (HCI) knowledge within the development team is essential. Failure to provide good usability can lead to project failure, undermining user satisfaction and adoption of the technology
Hybrid model for detection of brain tumor using convolution neural networks
Full text linkThe development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively
Adversarial attacks in signature verification: a deep learning approach
Full text linkHandwritten signature recognition in forensic science is crucial for identity and document authentication. While serving as a legal representation of a person’s agreement or consent to the contents of a document, handwritten signatures determine the authenticity of a document, identify forgeries, pinpoint the suspects and support other pieces of evidence like ink or document analysis. This work focuses on developing and evaluating a handwritten signature verification system using a convolutional neural network (CNN) and emphasising the model’s efficacy using hand-crafted adversarial attacks. Initially, handwritten signatures have been collected from sixteen volunteers, each contributing ten samples, followed by image normalization and augmentation to boost synthetic data samples and overcome the data scarcity. The proposed model achieved a testing accuracy of 91.35% using an 80:20 train-test split. Additionally, using the five-fold cross-validation, the model achieved a robust validation accuracy of nearly 98%. Finally, the introduction of manually constructed adversarial assaults on the signature images undermines the model’s accuracy, bringing the accuracy down to nearly 80%. This highlights the need to consider adversarial resilience while designing deep learning models for classification tasks. Exposing the model to real look-alike fake samples is critical while testing its robustness and refining the model using trial and error methods
Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset
Full text linkThis study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adaboost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with Adaboost being an exception that shows a decrease in accuracy after applying imbalanced data methods