Fiber Orientation In Short Fiber Reinforced Thermoplastic Composites

The objective of this project basically is to study the fiber orientation in short fiber reinforced thermoplastic composites especially at the geometry where the convergence and divergence flow is expected to occur. The material used for the study is chopped glass fiber 3mm in length as the reinforcement and high density polyethylene as the matrix. The study also includes a comparison of fiber orientation and the mechanical properties of the composites with different volume fraction of glass fiber. The volume fraction used is 5%, I 0%, 15% and 20% of glass fiber. The mold for the sample is using the standard shape for tensile test or called "dumbbell" shape. The advantage of this mold is able to investigate both the fiber orientation and the mechanical properties using the same mold geometry. The samples are produced using injection molding process with controlled parameters. The samples are characterized using micrographic test for the fiber orientation study and tensile test for the mechanical properties determination. The results show the fiber orientation in the sample confirm with the expected result with slight variation due to processing effects. While for the mechanical test show as the volume fraction increase the Young Modulus also increase. Therefore can be concluded that volume fraction of glass fiber have an effects to the fiber orientation and mechanical properties of the composite

A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network

The dynamics of computer networks have changed rapidly over the past few years due to a tremendous increase in the volume of the connected devices and the corresponding applications. This growth in the network’s size and our dependence on it for all aspects of our life have therefore resulted in the generation of many attacks on the network by malicious parties that are either novel or the mutations of the older attacks. These attacks pose many challenges for network security personnel to protect the computer and network nodes and corresponding data from possible intrusions. A network intrusion detection system (NIDS) can act as one of the efficient security solutions by constantly monitoring the network traffic to secure the entry points of a network. Despite enormous efforts by researchers, NIDS still suffers from a high false alarm rate (FAR) in detecting novel attacks. In this paper, we propose a novel NIDS framework based on a deep convolution neural network that utilizes network spectrogram images generated using the short-time Fourier transform. To test the efficiency of our proposed solution, we evaluated it using the CIC-IDS2017 dataset. The experimental results have shown about 2.5% - 4% improvement in accurately detecting intrusions compared to other deep learning (DL) algorithms while at the same time reducing the FAR by 4.3%-6.7% considering binary classification scenario. We also observed its efficiency for a 7-class classification scenario by achieving almost 98.75% accuracy with 0.56% - 3.72% improvement compared to other DL methodologies

Attitude, knowledge and competency towards precision agricultural practice among paddy farmers

Previous research indicated that farmers’ attitude, knowledge and competency influenced the adoption of technology. Realizing the truth, this study was carried out with the purpose to find factors and answers to questions that influenced the adoption of Precision Agricultural Practice (PAP). The study was carried out on 119 paddy farmers at IADA Barat Laut, Selangor. Results indicated that attitude, knowledge and competency significantly influenced the adoption of the PAP. Hence, to change the farmers’ attitude, knowledge and competency are vital aspects in the adoption of the PAP

Network Intrusion Detection System:A systematic study of Machine Learning and Deep Learning approaches

The rapid advances in the internet and communication fields have resulted in ahuge increase in the network size and the corresponding data. As a result, manynovel attacks are being generated and have posed challenges for network secu-rity to accurately detect intrusions. Furthermore, the presence of the intruderswiththeaimtolaunchvariousattackswithinthenetworkcannotbeignored.Anintrusion detection system (IDS) is one such tool that prevents the network frompossible intrusions by inspecting the network traffic, to ensure its confidential-ity, integrity, and availability. Despite enormous efforts by the researchers, IDSstillfaceschallengesinimprovingdetectionaccuracywhilereducingfalsealarmrates and in detecting novel intrusions. Recently, machine learning (ML) anddeep learning (DL)-based IDS systems are being deployed as potential solutionsto detect intrusions across the network in an efficient manner. This article firstclarifiestheconceptofIDSandthenprovidesthetaxonomybasedonthenotableML and DL techniques adopted in designing network-based IDS (NIDS) sys-tems. A comprehensive review of the recent NIDS-based articles is provided bydiscussing the strengths and limitations of the proposed solutions. Then, recenttrends and advancements of ML and DL-based NIDS are provided in terms ofthe proposed methodology, evaluation metrics, and dataset selection. Using theshortcomings of the proposed methods, we highlighted various research chal-lenges and provided the future scope for the research in improving ML andDL-based NIDS

Vulnerability Prevention Model for Web Browser using Interceptor Approach

The poster was submitted as part of the UNIMAS R&D Expo 2015. The project won Bronze Medal from the Expo for the ICT Cluster category

A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network

The dynamics of computer networks have changed rapidly over the past few years due to a tremendous increase in the volume of the connected devices and the corresponding applications. This growth in the network’s size and our dependence on it for all aspects of our life have therefore resulted in the generation of many attacks on the network by malicious parties that are either novel or the mutations of the older attacks. These attacks pose many challenges for network security personnel to protect the computer and network nodes and corresponding data from possible intrusions. A network intrusion detection system (NIDS) can act as one of the efficient security solutions by constantly monitoring the network traffic to secure the entry points of a network. Despite enormous efforts by researchers, NIDS still suffers from a high false alarm rate (FAR) in detecting novel attacks. In this paper, we propose a novel NIDS framework based on a deep convolution neural network that utilizes network spectrogram images generated using the short-time Fourier transform. To test the efficiency of our proposed solution, we evaluated it using the CIC-IDS2017 dataset. The experimental results have shown about 2.5% − 4% improvement in accurately detecting intrusions compared to other deep learning (DL) algorithms while at the same time reducing the FAR by 4.3%−6.7% considering binary classification scenario. We also observed its efficiency for a 7-class classification scenario by achieving almost 98.75% accuracy with 0.56% − 3.72% improvement compared to other DL methodologies

Unveiling the Incidence of Interfirm Collaboration: Evidence from Research and Development Companies in Malaysia

Nowadays, interfirm collaboration has become an increasingly popular strategy among many organizations in various industries, in order to remain competitive. Based on the contingency theory, this paper examines the moderating effect of interfirm collaboration on the relationship between Human Resource Management (HRM) practices and organizational performance. Interfirm collaboration refers to the collaboration strategies undertaken by R&D companies, with other companies in similar or diverse functional areas, including R&D, marketing, or manufacturing, to enhance performance.  Using data from 64 R&D companies, the hierarchical regression analyses showed that only collaboration in R&D and functional collaboration in manufacturing significantly moderated the relationship between HRM practices and organizational performance. Overall, the results provided partial support in the domain of the contingency theory. These results, however, are limited by the small sample size, which might have produced non-significant findings. Therefore, the generalization should be taken cautiously. Future research with a larger sample size is needed to confirm the findings

Unveiling the Incidence of Interfirm Collaboration: Evidence from Research and Development Companies in Malaysia

Nowadays, interfirm collaboration has become an increasingly popular strategy among many organizations in various industries, in order to remain competitive. Based on the contingency theory, this paper examines the moderating effect of interfirm collaboration on the relationship between Human Resource Management (HRM) practices and organizational performance. Interfirm collaboration refers to the collaboration strategies undertaken by R&D companies, with other companies in similar or diverse functional areas, including R&D, marketing, or manufacturing, to enhance performance.  Using data from 64 R&D companies, the hierarchical regression analyses showed that only collaboration in R&D and functional collaboration in manufacturing significantly moderated the relationship between HRM practices and organizational performance. Overall, the results provided partial support in the domain of the contingency theory. These results, however, are limited by the small sample size, which might have produced non-significant findings. Therefore, the generalization should be taken cautiously. Future research with a larger sample size is needed to confirm the findings

TPAAD: two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network.

Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial-of-service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two-Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K-nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two-Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead