Research on heteregeneous data for recognizing threat

The information increasingly large of volume dataset and multidimensional data has grown rapidly in recent years. Inter-related and update information from security communities or vendor network security has present of content vulnerability and patching bug from new attack (pattern) methods. It given a collection of datasets, we were asked to examine a sample of such data and look for pattern which may exist between certain pattern methods over time. There are several challenges, including handling dynamic data, sparse data, incomplete data, uncertain data, and semistructured/unstructured data. In this paper, we are addressing these challenges and using data mining approach to collecting scattered information in routine update regularly from provider or security community

ATLAS: Adaptive Text Localization Algorithm in High Color Similarity Background

One of the major problems that occur in text localization process is the issue of color similarity between text and background image. The limitation of localization algorithms due to high color similarity is highlighted in several research papers. Hence, this research focuses towards the improvement of text localizing capability in high color background image similarity by introducing an adaptive text localization algorithm (ATLAS). ATLAS is an edge-based text localization algorithm that consists of two parts.  Text-Background Similarity Index (TBSI) being the first part of ATLAS, measures the similarity index of every text region while the second, Multi Adaptive Threshold (MAT), performs multiple adaptive thresholds calculation using size filtration and degree deviation for locating the possible text region. In this research, ATLAS is verified and compared with other localization techniques based on two parameters, localizing strength and precision. The experiment has been implemented and verified using two types of datasets, generated text color spectrum dataset and Document Analysis and Recognition dataset (ICDAR). The result shows ATLAS has significant improvement on localizing strength and slight improvement on precision compared with other localization algorithms in high color text-background image

Attack and Vulnerability Penetration Testing: FreeBSD

 Computer system security has become a major concern over the past few years. Attacks, threats or intrusions, against computer system and network have become commonplace events. However, there are some system devices and other tools that are available to overcome the threat of these attacks. Currently, cyber attack is a major research and inevitable. This paper presents some steps of penetration in FreeBSD operating system, some tools and new steps to attack used in this experiment, probes for reconnaissance, guessing password via brute force, gaining privilege access and flooding victim machine to decrease availability. All these attacks were executed and infiltrate within the environment of Intrusion Threat Detection Universiti Teknologi Malaysia (ITD UTM) data set. This work is expected to be a reference for practitioners to prepare their systems from Internet attacks

Ke Arah Impian Menggapai Taraf Universiti Penyelidikan di Malaysia: Kajian Kes Universiti Malaysia Sabah

The Ministry of Higher Education of Malaysia has recently called for all public universities in Malaysia to focus more on research activities. This call is to ensure that a high-quality standard of research can be achieved in order to produce more research universities in the future. Accordingly, Malaysia University of Sabah (UMS), as the ninth public university in Malaysia, has enthusiastically answered this call by encouraging and facilitating its academic staff to actively engage in research activities. UMS, however, realizes that it will take a long process before it can be regarded as a research university. Therefore, this article has identified six pro-active actions that may be taken by UMS to achieve its vision i.e. (1) strengthen physical needs; (2) increase the number of postgraduate students; (3) acquire more highly qualified needs; (3) acquire more highly qualified academicians; (4) enhance international networking; (5) form several research leaders or “Malim Sarjana”; and (6) publish and commercialize research outcomes in highly refereed journals and commercial sectors. Especially the latest, UMS should cooperate in publishing sector with some foreign universities to make its name is acknowledged and well-known by international world. Without the effort of productive and qualified publication, this beautiful and prominent infrastructure will not contribute to establishing a pioneer and superior university in research field

IoT Smart Device for e-Learning Content Sharing on Hybrid Cloud Environment

Centralized e-Learning technology has dominated the learning ecosystem that brings a lot of potential usage on media rich learning materials. However, the centralized architecture has their own constraint to support large number of users for accessing large size of learning contents. On the other hand, Content Delivery Network (CDN) solution which relies on distributed architecture provides an alternative solution to eliminate  bottleneck  access.  Although  CDN  is   an  effective solution, the implementation of technology is expensive and has less impact for student who lives in limited or non-existence internet access in geographical area. In this paper, we introduce an IoT smart device to provide e-Learning access for content sharing on hybrid cloud environment with distributed peer-to- peer communication solution for data synchronization and updates. The IoT smart device acts as an intermediate device between user and cloud services, and provides content sharing solution without fully depending on the cloud server

Text localization in images using reverse thresholds algorithm

High color similarity between text pixels and background pixels is the major problem that causes failure during text localization. In this paper, a novel algorithm, Reverse Thresholds (RT) algorithm is proposed to localize text from the images with various text-background color similarities. First, a rough calculation is proposed to determine the similarity index for every text region. Then, by applying reverse operation, the best thresholds for each text region are calculated by its similarity index. To remove other uncertainties, self-generated images with the same text features but different similarity index are used as experiment dataset. Experiment result shows that RT algorithm has higher localizing strength which is able to localize text in a wider range of similarity index

IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results

Evaluating the Impact of Usability Components on User Satisfaction in Educational Board Games using the MEEGA+ Framework

Conventional instructional methods often fail to achieve significant learning outcomes and user satisfaction, making educational board games (EBGs) a dynamic and engaging alternative. This study aims to analyze the impact of usability components (aesthetics, learnability, and operability) on user satisfaction in EBGs using the MEEGA+ framework. A purposive sampling technique was used to select bachelor’s degree students enrolled in the Personal Financial Planning (PFP) subject. A quantitative study was conducted using self-administered questionnaires, and the data were analyzed using SPSS version 29.0. The findings indicated that usability components (aesthetics, learnability, and operability) significantly influenced user satisfaction in EBGs for PFP students. The study suggests incorporating game-based learning into curricula to enhance understanding and satisfaction. It also highlights the importance of usability components in EBGs, providing a theoretical framework for future research in game theory, cognitive, and pedagogical approaches

Exploring the Usability and Engagement of Students in an Educational Board Game on Personal Financial Planning

Traditional teaching approaches lack engagement, personalization, flexibility, and effective evaluation, thereby limiting learning outcomes. Consequently, these systems face challenges in engaging students, meeting their individual needs, adapting to diverse learning situations, and accurately assessing their competency. Educational board games (EBGs) offer a dynamic and engaging learning experience through play and personalized learning, promising optimal outcomes to overcome these obstacles. The objective of this study is to investigate how usability influences student engagement using the MEEGA+ evaluation approach during EBG play in a Personal Financial Planning course at Universiti Teknologi MARA (UiTM) Cawangan Melaka, Kampus Bandaraya Melaka. This quantitative study involved a self-administered questionnaire and a purposive sample of 200 bachelor’s degree students in the course. PLS-SEM was utilized to assess the model and hypothesized relationships. The operability and accessibility of educational games significantly enhanced student engagement, underscoring the utility of board games for learning through play. Usability, experience, and engagement should be integral considerations in instructional game design. The MEEGA+ assessment model can be applied to evaluate game-based learning across various EBGs. Board games have the potential to enhance educational outcomes, underscoring the necessity for further research in game-based learning. The study also suggests that the MEEGA assessment model can assist designers and educators in developing effective game-based learning experiences

Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods