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"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race."

"The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise."

"Arsitektur 5G Core (5GC) menjawab permintaan akan koneksi berkecepatan tinggi dan aman dengan janji konektivitas yang lebih cepat dan keandalan jaringan yang lebih baik. Namun, tantangan keamanan siber terhadap serangan pada Session Management Function (SMF) melalui Packet Forwarding Control Protocol (PFCP) mendorong pengembangan Intrusion Detection System (IDS) menggunakan Machine Learning. Dataset yang digunakan dalam penelitian adalah 5G Core PFCP Intrusion Dataset milik George Amponis, dkk. Penelitian dilakukan dengan menggunakan metode fitur seleksi seperti filter dengan korelasi Pearson, embedded, dan wrapper dengan Recursive Feature Elimination (RFE). Model Machine Learning yang diujikan adalah Random Forest, Gradient Boost Machine (GBM), Light Gradient Boost Machine (LGBM), Extreme Gradient Boost (XGB), dan AdaBoost. Skenario penelitian dibuat menjadi dua berdasarkan data awal dari 5G Core PFCP Intrusion Dataset dengan lima kelas target dan skenario setelah dilakukan penggabungan pada serangan PFCP Session Modification Flood Attack menjadi empat kelas target. Penelitian mendapatkan bahwa kombinasi model GBM dengan metode seleksi fitur embedded pada skenario empat kelas target memiliki kinerja terbaik dalam mendeteksi serangan PFCP pada jaringan 5G Core dengan nilai akurasi sebesar 97,366%, presisi 97,383%, recall 97,366%, dan f1-score sebesar 97,375%.

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The 5G Core (5GC) architecture addresses the demand for high-speed and secure connections with the promise of faster connectivity and better network reliability. However, cybersecurity challenges against attacks on the Session Management Function (SMF) through the Packet Forwarding Control Protocol (PFCP) drive the development of an Intrusion Detection System (IDS) using Machine Learning. The dataset used in the research is the 5G Core PFCP Intrusion Dataset by George Amponis, et al. Research was conducted using feature selection methods such as filters with Pearson correlation, embedded, and wrapper with Recursive Feature Elimination (RFE). The Machine Learning models tested were Random Forest, Gradient Boost Machine (GBM), Light Gradient Boost Machine (LGBM), Extreme Gradient Boost (XGB), and AdaBoost. The research scenarios were made into two based on the initial data from the 5G Core PFCP Intrusion Dataset with five target classes and the scenario after combining the PFCP Session Modification Flood Attack into four target classes. The research found that the combination of the GBM model with the embedded feature selection method in the four target classes scenario has the best performance in detecting PFCP attacks on the 5G Core network with an accuracy value of 97.366%, precision of 97.383%, recall of 97.366%, and f1-score of 97.375%."

"Meningkatnya jumlah pengguna internet saat ini memberikan banyak dampak pada kehidupan manusia, karena internet menghubungkan banyak perangkat setiap hari. Perkembangan ini membawa berbagai dampak positif maupun dampak negatif. Salah satu dampak negatifnya adalah adanya aktivitas berbahaya yang dapat menyerang jaringan. Intrusion detection system merupakan sebuah sistem manajemen keamanan pada jaringan komputer. Data yang dimiliki intrusion detection system mempunyai fitur yang cukup banyak tetapi tidak semua fitur yang ada relevan dengan data yang digunakan dan jika data tersebut diolah akan memakan waktu yang cukup lama. Oleh karena itu, diperlukan pemilihan fitur untuk meningkatkan akurasi serta memperpendek waktu pembelajaran. Beberapa metode pembelajaran sudah pernah diterapkan untuk menyelesaikan masalah intrusion detection system, seperti Na ? ve Bayes, Decision Tree, Support Vector Machines dan Neuro-Fuzzy Methods. Metode pemilihan fitur yang digunakan untuk skripsi ini adalah metode Chi-Square. Setelah dilakukan pemilihan fitur, akan didapatkan hasil berupa sebuah dataset baru yang kemudian akan diklasifikasi menggunakan metode Extreme Learning Machines. Hasilnya menunjukkan setelah dilakukan pemilihan fitur dengan metode Chi-Square, tingkat akurasi akan meningkat serta waktu yang dibutuhkan algoritma pembelajaran untuk menyelesaikan metode tersebut menjadi semakin singkat.

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The increasing rates of internet users nowadays must be give much impacts to our lifes, because the internet things can connect more devices every day. This growth carriers several benefits as well as can attack the network. Intrusion detection system IDS are used as security management system. IDS can be used to detect suspicious activity or alert the system. IDS involves large number of data sets with several different features but not all features are relevant with the data sets and it takes long computational time to solve IDS data sets. Therefore, it has to do feature selection to remove the irrelevant features, to increase the accuracy and to shorten the computational time for the learning methods.

Many researches about learning method to solve intrusion detection system problem have been done to develop and test the best model from various classifiers, such as Na ve Bayes, Decision Tree, Support Vector Machines, and Neuro Fuzzy Methods. For this thesis, the feature selection methods will be used is Chi Square methods to reduce dimentionality of IDS data sets. The new IDS data sets with the best selected features are obtained afterwards, and then these new data sets will be classified with Extreme Learning Machines methods. The result denotes that Extreme Learning Machines classification methods provides better accuracy level while combined with Chi Square feature selection."

"Just like electricity, Machine Learning will revolutionize our life in many ways-some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with."

"Kepolisian Republik Indonesia (Polri) merupakan alat negara yang terus berusaha memberikan pelayanan publik secara prima salah satu nya dengan melakukan inovasi dengan memanfaatkan teknologi dalam memberikan pelayanan SIM melalui aplikasi bernama Digital Korlantas Polri. Namun sejak aplikasi tersebut diluncurkan pada tahun 2021 hingga tahun 2022 terdapat pemberitaan di berita online terkait kendala pada aplikasi dalam perpanjangan SIM online yang tidak berjalan sebagaimana semestinya. Penelitian ini bertujuan untuk melihat bagaimana pandangan masyarakat sebagai pengguna layanan dari Twitter dan Play Store. Data yang digunakan dalam penelitian ini berasal dari Twitter dan Play Store sebanyak 5944 data. Analisis dilakukan dengan membangun model klasifikasi relevansi, aspek, dan sentimen pada aspek reliability, efficiency, trust, dan citizen support. Algoritma yang digunakan yaitu Decision Tree, Logistic Regression, dan SVM. Hasil pemodelan klasifikasi dengan performa yang paling tinggi dalam klasifikasi relevansi, aspek, dan sentimen pada tiap aspek dihasilkan oleh algoritma Logistic Regression dengan TF-IDF unigram dan SMOTE. Pada model klasifikasi relevansi didapatkan nilai accuracy sebesar 87.05%, precision sebesar 87.38%, recall sebesar 87.04%, dan f1 score sebesar 87.16%. Pada model klasifikasi aspek, nilai accuracy sebesar 74.28%, precision sebesar 75.93%, recall sebesar 74.27%, dan f1 score sebesar 74.70%. Pada model klasifikasi sentimen pada masing-masing aspek, model klasifikasi sentimen pada aspek citizen support mendapatkan nilai yang paling tinggi dibanding aspek lain yaitu dengan nilai accuration sebesar 95.38%, precision sebesar 95.60%, recall sebesar 95.38%, dan f1-score sebesar 94.05%. Pada penelitian ini menghasilkan temuan sentimen pada masing-masing aspek dalam layanan perpanjang SIM online di aplikasi Digital Korlantas Polri dimana reliability merupakan aspek yang paling banyak dikemukakan dan mendapat sentimen negatif, kemudian diikuti oleh aspek efficiency, citizen support, dan aspek trust.

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The Indonesian National Police (Polri) continues to strive to provide excellent public services, one of which is by innovating by utilizing technology in providing SIM services through an application called Digital Korlantas Polri. However, since the application was launched in 2021 to 2022 there have been reports in online news regarding problems with applications, so it is necessary to conduct research regarding how the public views the application as service users and maps these views into aspects which affect the quality of government services so that service providers can take improvement to realize excellent service delivery. The data used in this study are from Twitter and Play Store as many as 5944 data. The analysis is carried out by building a classification model of relevance, aspect, and sentiment on the aspects of reliability, efficiency, trust, and citizen support. The algorithms used are Decision Tree, Logistic Regression, and SVM. The results of classification modeling with the highest performance in the classification of relevance, aspect, and sentiment for each aspect were produced by the Logistic Regression algorithm with the TF-IDF unigram and SMOTE. In the relevance classification model, the accuracy value is 87.05%, precision is 87.38%, recall is 87.04%, and f1 score is 87.16%. In the aspect classification model, the accuracy value is 74.28%, precision is 75.93%, recall is 74.27%, and f1 score is 74.70%. In the sentiment classification model for each aspect, the sentiment classification model for the citizen support aspect gets the highest score compared to other aspects, namely with an accuracy value of 95.38%, a precision of 95.60%, a recall of 95.38%, and an f1-score of 94.05% . This study produced sentiment findings for each aspect of the online SIM service in the Digital Korlantas Polri application where reliability was the aspect that was most frequently raised and received negative sentiment, followed by aspects of efficiency, citizen support, and trust."

"With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book"

"Aktivitas produksi dan ekspor komoditas kelapa sawit terus mengalami ekspansi dan peningkatan. Indonesia memiliki perkebunan kelapa sawit dengan luas mencapai 12.761.586 Hektar. menjadikan Indonesia sebagai salah satu penghasil CPO (Crude Palm Oil) terbesar di dunia. Keberhasilan produksi dari kelapa sawit tidak terlepas dari kegiatan perencanaan dan pengawasan sehingga diperlukan pemantauan secara cepat dan efektif. Penelitian ini dilakukan dengan tujuan untuk mengetahui karakteristik dan pola persebaran umur kelapa sawit berdasarkan nilai backscatter pada citra radar Sentinel-1. Data berupa citra radar Sentinel-1 digunakakan untuk dapat melakukan estimasi terhadap umur kelapa sawit berdasarkan nilai backscatter menggunakan pendekatan machine learning. Hasil pemodelan menunjukan bahwa tren nilai backscatter terhadap umur kelapa sawit memiliki karakter berbanding lurus dengan umur kelapa sawit. Estimasi umur kelapa sawit berdasarkan nilai backscatter pada Sentinel-1 GRD menghasilkan 3 kelas umur kelapa sawit dengan tingkat overall accuracy sebesar 93.3% pada anlisis yang dilakukan secara Single Time, sedangkan pada analisis time series diperoleh nilai overall accuracy sebesar 94.5% Hasil menunjukkan bahwa kelas umur dewasa memiliki nilai z score sebesar -4.190963 dengan pola persebaran clustered (mengelompok), kelas umur taruna dengan z score -8.388942 berpola clustered (mengelompok), dan kelas umur remaja dengan perolehan nilai z score 7.801667 dengan pola persebaran dispersed (seragam).

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Production and export activities of palm oil commodities continue to expand and increase. Indonesia has oil palm plantations with an area of ​​12,761,586 hectares. making Indonesia one of the largest CPO (Crude Palm Oil) producers in the world. The success of production from oil palm cannot be separated from planning and monitoring activities so that it is necessary to monitor quickly and effectively. This research was conducted with the aim of knowing the characteristics and patterns of age distribution of oil palms based on the backscatter value on Sentinel-1 radar images. Data in the form of Sentinel-1 radar images are used to estimate the age of oil palms based on the backscatter value using a machine learning approach. The modeling results show that the trend of the backscatter value of the age of the oil palm has a character that is directly proportional to the age of the oil palm. Oil palm age estimation based on the backscatter value on Sentinel-1 GRD resulted in 3 oil palm age classes with an overall accuracy rate of 93.3% in the Single Time analysis, while the time series analysis obtained an overall accuracy value of 94.5%. adults have a z score of -4.190963 with a clustered distribution pattern, the cadet age class with a z score of -8.388942 with a clustered pattern, and the adolescent age class with a z score of 7.801667 with a dispersed distribution pattern."

"ABSTRACTBig data or Data driven farming have been the latest improvement in agricultural sector. Data driven farming allows farmers to maximize the output of harvest by processing any significant data gathered regarding the crop. With the data of the crop available, it opens the possibility of evaluating the data to make a model for the crop. This model will allow predictions to be made which would improve the data driven farming to an extent. This project is based on improving Farmbot, a data driven farming tool, to allow the makings of a prediction based on sensor readings gathered by the tool. Several machine learning algorithms have been evaluated which takes account two sensor reading of the plant, and performances have been discussed. These parameters include soil moisture and light exposure level and the performance level gauged are predictability and interpretability. Based on the said parameters, Decision Tree Machine Learning Algorithm have been deemed the best method of prediction for a 2 class problem. This is based on its ability to make a prediction with relatively high confidence level with the addition of having high interpretability about how the algorithm come to the said conclusion. Decision Trees current state may be improved by implementing tree pruning method to omit unnecessary splits.

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ABSTRACTBig-Data Farming atau pertanian berbasis data merupakan perkembangan mutakhir pada sektor agrikultur. Dengan berbasis data mengenai asupan cahaya dan tingkat kelembaban, petani dapat memaksimalkan hasil panen dari suatu tanaman dengan memproses data mengenai tanaman tersebut. Dengan menyediakan data mengenai tanaman, hal ini memungkinkan pengolahan data dan membuat model yang menggambarkan pengaruh data ndash; data yang diperoleh dengan hasil panen suatu tanaman. Proyek ini dilaksanakan atas dasar mengembangkan sistem Farmbot, sebuah alat tanam automatis berbasis data, untuk menyediakan prediksi tentang bagaimana hasil panen tanaman tersebut berdasarkan data yang diperoleh dari sensor yang terdapat pada alat tersebut. Kemampuan Farmbot untuk melakukan perdiksi tersebut bisa dilakukan dengan mengimplementasikan algoritma Machine Learning, Dengan adanya berbagai macam algoritma Machine Learning, pemilihan algoritma yang paling tepat untuk implementasi Farmbot juga merupakan salah satu bahan pembahasan. Berhubung 2 parameter yang telah disebutkan merupakan kunci dari pembuatan model prediksi, algoritma Decision Tree dianggap sebagai algoritma yang paling optimal untuk diimplementasikan. Keputusan ini berdasarkan kemampuan Decision Tree dalam membuat prediksi dengan tingkat keyakinan yang tinggi dan juga berkemampuan untuk menggambarkan langkah langkah yang ditempuh untuk mencapai suatu prediksi. Algoritma Decision Tree yang telah diimplementasikan pada Farmbot dapat ditingkatkan dengan mengimplementasikan metode Tree Pruning untuk menghilangkan perpisahan yang tidak dibutuhkan."