Judul: Identification of Android APK Malware through Local and  Global Feature Extraction using Meta Classifier
Abstrak: Android merupakan sistem operasi perangkat cerdas yang paling banyak digunakan saat ini. Tingginya popularitas Android menjadikannya sebagai platform yang paling rentan terhadap persebaran malware. Untuk meningkatkan keamanan perangkat, teknik analisis statis dan dinamis dapat digunakan. Pembuatan malware saat ini menjadi semakin canggih yang didukung oleh perkembangan framework. yang ada. Beberapa teknik pembuatan malware dapat dikerjakan diantaranya adalah teknik pengemasan code program, pengaburan code program, dan teknik enkripsi untuk menghindari model pada sistem deteksi. Kemudahan dalam melakukan kustomisasi code program dan kompleksitas struktur komponen dalam satu berkas APK menjadikan teknik deteksi tradisional tidak lagi dapat diandalkan untuk mendeteksi malware yang baru disebar. Berbagai penelitian telah dikerjakan menggunakan metode kecerdasan buatan yang diklaim mampu mendeteksi malware secara cepat dan fleksibel dengan tingkat akurasi yang tinggi. Penelitian ini bertujuan untuk melakukan perbandingan deteksi malware Android menggunakan kombinasi meta-classifier yang menggabungkan base classifier LightGBM, Support Vector Machine dan Random Forest. Selain menggabungkan base-classifer, penelitian ini juga melihat pengaruh fitur lokal dan global serta gabungannya terhadap performa dari model yang dihasilkan. Data yang digunakan adalah dataset malware APK Android utuh yang diunduh pada website Canadian Institute for Cybersecurity. Dataset malware APK Android dilakukan proses deteksi malware menggunakan pendekatan Machine Learning pada data yang sudah divisualkan. Teknik ini dapat digunakan untuk mendeteksi malware tanpa perlu melakukan analisis mendalam. Tahapan dari teknik ini adalah merubah berkas classes.dex yang berada dalam berkas APK menjadi citra grayscale yang kemudian fiturnya diekstraksi menggunakan algoritma ekstraksi fitur global maupun lokal. Fitur citra tersebut kemudian diproses menggunakan meta-classifier untuk mengklasifikasikan model deteksi malware. Penelitian ini berhasil melakukan praproses data, transformasi citra, ekstraksi fitur lokal dan global dan pembentukan model prediksi malware. Hasil uji keseluruhan dari model ini memuaskan. Nilai Akurasi dan F1 Score mendapatkan nilai terbaik pada 97% yaitu pada model deteksi yang merupakan model gabungan antar classifier yang bekerja pada gabungan ekstraksi fitur global dan lokal. Model prediksi meta-classifier yang dihasilkan sangat penting untuk melakukan deteksi malware dengan cepat dan akurat sebagai upaya pencegahan dari kejahatan siber yang merugikan masyarakat luas., Android is the most widely used operating system for smart devices today. The high popularity of Android makes it the most vulnerable platform to the spread of malware. Static and dynamic analysis techniques can be used to improve device security. Malware creation is becoming more sophisticated, supporting the development of existing frameworks. Several malware creation techniques can be worked on, including program code packaging techniques, program code obfuscation, and encryption techniques to avoid models in the detection system. The ease of customizing program code and the complexity of the component structure in one APK file make traditional detection techniques no longer reliable for detecting newly distributed malware. Various studies have been done using artificial intelligence methods that are claimed to be able to detect malware quickly and flexibly with a high level of accuracy. This research aims to compare Android malware detection using a combination of meta-classifiers that combine the LightGBM base-classifier, Support Vector Machine, and Random Forest. In addition to combining base classifiers, this research also looks at the effect of local and global features and their combination on the performance
Keyword: APK Android Visualization, Global Feature Extraction, Local Feature  Extraction, Malware, Meta Classifier

Judul: Deteksi Malware Berbasis System Call dengan Klasifikasi Support Vector Machine pada Android
Abstrak: Android is an operating system that is widely used in smartphone. The biggest threat in Android is the spread of malware that comes from Android application stores. Generally, malware use signature-based method which can be easily avoided by malware that has polymorphic capabilities. Thus, it requires more dynamic detection method. The purpose of this research is to see whether system calls can be used as features to detect Android malware and to test the accuracy of the Support Vector Machine (SVM) in classifying malware and non-malware applications using system call frequencies. The frequencies of system calls were obtained from the result of executing Android applications and unused system calls were excluded. After that, the Principal Component Analysis process was conducted to reduce the dimension and eliminate the irrelevant features. The use of Radial Basis Function kernel in SVM achieves 86.25% of malware classification while the polynomial kernel achieves 90% of malware classification.
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Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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