The effect of sodium borate addition to dimentional stability in type 3 of gypsum

Background: The removable dentures or fixed dentures can be used as a replacement for missing teeth and restore the aesthetics and functional condition of the patien. Dentures with good adaptation can be obtained from accurate impressions. Dimensional accuracy and adequate mechanical properties are one of the main requirements of working model making materials. The addition of sodium borate can help gypsum type III achieve balance dimentional stability. The purpose of this study was to determine the difference in dimensional stability of gypsum type III with aquades with the addition of sodium borate and aquades without the addition of sodium borate.Method: Post test only control group design was used as the research design method. This study consisted of 2 groups with a total sample of 32 samples, experimental group with gypsum type III given distilled water with added sodium borate 0.4% and control group with gypsum type III given distilled water without added sodium borate 0.4%. The change of gypsum dimentional stability is measured using digital sliding caliper. Statistical tests were carried out using the Mann Whitney test and the results of the Mann Whitney test were P < 0.05 which means a significant difference between the two groups. Result: The lowest average dimensional change was found in gypsum type III mixed with 0.4% sodium borate.Conclusion: The conclusion of the research is the addition of 0,4% sodium borate solution can help gypsum type III achieve balance dimentional stability

Time Efficiency on Computational Performance of PCA, FA and TSVD on Ransomware Detection

Ransomware is able to attack and take over access of the targeted user'scomputer. Then the hackers demand a ransom to restore the user's accessrights. Ransomware detection process especially in big data has problems interm of computational processing time or detection speed. Thus, it requires adimensionality reduction method for computational process efficiency. Thisresearch work investigates the efficiency of three dimensionality reductionmethods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) andTruncated Singular Value Decomposition (TSVD). Experimental results onCICAndMal2017 dataset show that PCA is the fastest and most significantmethod in the computational process with average detection time of 34.33s.Furthermore, result of accuracy, precision and recall also show that the PCAis superior compared to FA and TSVD

Network anomaly detection research: a survey

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies

Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%

Time efficiency on computational performance of PCA, FA and TSVD on ransomware detection

Ransomware is able to attack and take over access of the targeted user's computer. Then the hackers demand a ransom to restore the user's access rights. Ransomware detection process especially in big data has problems in term of computational processing time or detection speed. Thus, it requires a dimensionality reduction method for computational process efficiency. This research work investigates the efficiency of three dimensionality reduction methods, i.e.: Principal Component Analysis (PCA), Factor Analysis (FA) and Truncated Singular Value Decomposition (TSVD). Experimental results on CICAndMal2017 dataset show that PCA is the fastest and most significant method in the computational process with average detection time of 34.33s. Furthermore, result of accuracy, precision and recall also show that the PCA is superior compared to FA and TSVD

Deteksi Malware Ransomware Menggunakan Deep Neural Network

Malware pada perangkat mobile android menjadi sebuah tantangan yang perlu di perhatikan secara khusus. Mengingat akhir-akhir ini banyak kasus kejahatan dalam teknologi informasi dan komunikasi melalui malware.  Sebuah malware ini bertujuan untuk mencuri, mengenkripsi, dan menghapus data sensitif kemudian mengubah atau membajak data dari sebuah perangkat pengguna. Oleh karena itu, pada penelitian ini bertujuan untuk mendeteksi malware jenis ransomware melalui system operasi android menggunakan metode deep learning. Metode yang diusulkan pada penelitian ini adalah Deep Neural Network (DNN). Dataset CIC-InvesAndMal2019 akan diujikan ke model hasil dari proses training DNN. Hasil pengujian model DNN menunjukkan bahwa DNN berhasil mendeteksi malware ransomware dengan tingkat akurasi mencapai 96.6 %

Features extraction on iot intrusion detection system using principal components analysis (PCA)

Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent

Enhanced deep learning intrusion detection in IoT heterogeneous network with feature extraction

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%