1,720,955 research outputs found
Model Klasifikasi Serangan DDoS pada Layer Aplikasi Menggunakan Metode Random Forest
No full textDistributed Denial of Service(DDoS) masih menjadi topik yang diteliti dalam penelitian tentang keamanan jaringan. Hal itu disebabkan semakin banyak tools untuk melakukan serangan DDoS dan semakin mudah cara penggunaannya. Pada kuarter pertama tahun 2018 tercatat rata-rata ukuran terbesar serangan DDoS mencapai 11.2 Gbps. Layer aplikasi menjadi salah satu target untuk melancarkan serangan DDoS, dengan memanfaatkan celah pada protokol yang berjalan pada layer aplikasi penyerang melakukan serangan untuk meniadakan layanan server untuk user. Serangan DDoS pada layer aplikasi memiliki tingkat kesulitan yang berbeda karena serangannya hampir menyerupai request user pada umumnya, jika dibandingkan serangan DDoS pada layer network dan transport yang menyerang dengan membanjiri trafik jaringan. Serangan pada layer tersebut terbukti mampu melewati keamanan yang tertanam pada perangkat jaringan.
Oleh karena itu, penelitian ini berfokus pada klasifikasi serangan DDoS pada layer aplikasi. Model klasifikasi yang dibangun pada penelitian ini menggunakan dataset CICIDS2017 yang disediakan oleh University of New Brunswick (UNB). Metode random forest dipilih pada penelitian ini dikarenakan keunggulan dari metode tersebut dan dianggap cocok untuk mengklasifikasi serangan DdoS pada layer aplikasi. Beberapa keunggulan metode random forest adalah mampu mengatasi data yang besar, memiliki akurasi yang tinggi, dan tidak terpengaruh oleh missing data.
Penelitian ini berhasil membangun model klasifikasi dengan akurasi sebesar 0.9993. Nilai recall untuk kelas normal sebesar 0.9999, DDoS goldeneye sebesar 0.9946, DDoS hulk sebesar 0.9985, DDoS slowhttptest sebesar 0.9972, dan DDoS slowloris sebesar 0.9976. Nilai presisi untuk kelas normal sebesar 0.9992, kelas DDoS goldeneye sebesar 0.9979, kelas DDoS hulk sebesar 0.9999, kelas DDoS slowhttptest sebesar 0.9925, dan kelas DDoS slowloris sebesar 0.9939. Lima Importance variable yang didapat dari model klasifikasi random forest adalah backward packet length max, max packet length, subflow backward bytes, total length of backward packets, destination port
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Performance Comparison of LSTM Models with Various Optimizers and Activation Functions for Garlic Bulb Price Prediction Using Deep Learning
Get PDFAccurate commodity price forecasting is crucial for market stability and decision-making. This study evaluates the performance of the Long Short-Term Memory (LSTM) model using various activation functions and optimization algorithms for predicting garlic bulb prices. Historical price data was collected from panelharga.badanpangan.go.id and preprocessed through normalization and dataset splitting into training, validation, and test sets. The model was trained for 200 epochs using activation functions ReLU, Sigmoid, and Tanh, combined with optimization algorithms Adam, RMSprop, SGD, Adagrad, Adadelta, Nadam, and AdamW. Experimental results indicate that ReLU + Adam achieves the best performance with Final Epoch Loss of 0.001789, RMSE of 0.701632, MAPE of 0.009593, and R² of 0.909794, followed by Sigmoid + Nadam and Tanh + Adam, which also yielded high accuracy. These findings reinforce prior research, highlighting Adam and its momentum-based variants as effective optimizers for LSTM training. This study provides insights into selecting optimal activation functions and optimizers for commodity price forecasting. Future work may explore hybrid models and external factors, such as global market trends, to enhance predictive accuracy in time series data analysis
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models
Get PDFAccurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS). Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259.52, an RMSE of 154,036.91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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