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Detection of Cyber Malware Attack Based on Network Traffic Features Using Neural Network Ventje Jeremias Lewi Engel; Evan Joshua; Mychael Maoeretz Engel
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8869

Abstract

Various techniques have been developed to detect cyber malware attacks, such as behavior based method which utilizes the analysis of permissions and system calls made by a process. However, this technique cannot handle the types of malware that continue to evolve. Therefore, an analysis of other suspicious activities – namely network traffic or network traffic – need to be conducted. Network traffic acts as a medium for sending information used by malware developers to communicate with malware infecting a victim's device. Malware analyzed in this study is divided into 3 classes, namely adware, general malware, and benign. The malware classification implements 79 features extracted from network traffic flow and an analysis of these features using a Neural Network that matches the characteristics of a time-series feature. The total flow of network traffic used is 442,240 data. The results showed that 15 main features selected based on literature studies resulted in F-measure 0.6404 with hidden neurons 12, learning rate 0.1, and epoch 300. As a comparison, the researchers chose 12 features based on the nature of the malware possessed, with the F-measure score of 0.666 with hidden neurons 12, learning rate 0.05, and epoch 300. This study found the importance of data normalization technique to ensure that no feature was far more dominant than other features. It was concluded that the analysis of network traffic features using Neural Network can be used to detect cyber malware attacks and more features does not imply better detection performance, but real-time malware detection is required for network traffic on IoT devices and smartphones.
Penerapan NFC Untuk Pembayaran Uang Elektronik pada Self-Payment Machine Ventje Jeremias Lewi Engel; Vincentius Albert; Sinung Suakanto
Jurnal Telematika Vol 12, No 1 (2017)
Publisher : Institut Teknologi Harapan Bangsa

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Abstract

Consumers nowadays use electronic money for buying and selling at the market. They need easier and faster payment system especially to facilitate the needs in payment on delivery. Therefore, a self-payment machine were designed and implemented with NFC to bring out faster and easier transaction. The system was tested for its functionality, time, and distance between tag and NFC reader.  Self-payment machine can communicate with payment gateway, execute transaction, and evaluate tag used for paying. Time average for a transaction is 27.3345 seconds with 4-5 seconds needed for reading and evaluating the tag. Optimal distance between tag and reader is 0-4 cm.Penggunaan uang elektronik sudah menjadi kebutuhan bagi para pelaku transaksi. Konsumen memerlukan sistem pembayaran yang cepat dan mudah terutama untuk memfasilitasi kebutuhan ketika payment on delivery.. Oleh sebab itu, dirancang sebuah self- payment machine yang berbasis NFC untuk dapat mempermudah dan mempercepat transaksi. Sistem yang dibangun diuji secara fungsionalitas, waktu, dan jarak pembacaan tag NFC.  Sistem self-payment machine yang dibangun mampu berkomunikasi dengan payment gateway, melakukan transaksi, dan mengevaluasi tag untuk pembayaran. Rata-rata waktu yang diperlukan untuk transaksi adalah 27,3345 dengan pembacaan dan evaluasi tag memerlukan 4-5 detik. Jarak optimal antara tag dengan pembaca NFC adalah 0-4 cm.
Sistem Pengawasan Kinerja Jaringan Server Web Apache dengan Log Management System ELK (Elasticsearch, Logstash, Kibana) Claudia Tarigan; Ventje Jeremias Lewi Engel; Dina Angela
Jurnal Telematika 2018: Industrial Engineering Seminar and Call for Paper (IESC) 2018
Publisher : Institut Teknologi Harapan Bangsa

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Abstract

A server is a software that has a duty and responsibility to provide information to the web. Any process that occurs within the web server will be recorded in a log. A log is a file that contains a list of actions, events (activities) that have been going on in a computer system. By using the log files on a web server, a lot of things that can be done by system administrators to monitor the performance of the web server. However, the log on the web server are difficult to understand and read. Log management system is a system that can handle data services log in large numbers and generate detailed log information. Logstash is the application log management system that can help the system administrator in the performance monitor log of the web server. Logstash will be combined with the Elasticsearch serves as data storage media and Kibana as visualization. ELK (Elasticsearch, Logstash, Kibana) is used to display and keep an eye on the log data from web server so, system administrators can know the performance of the web server.Server sebagai penyedia layanan di jaringan memastikan agar semua aktivitas yang berkaitan dengannya dicatat dalam log. Log adalah file yang berisi daftar aktivitas dan waktu yang terjadi dalam server. Pemantauan kinerja server dengan memanfaatkan log akan meningkatkan ketahanan dan tingkat layanan sebuah server. Masalahnya adalah log yang tercatat tersebar di beberapa tempat dan susah untuk langsung diolah karena bentuknya bukan seperti tabel. Log management system dapat menangani data log dalam jumlah besar, menghasilkan detail informasi aktivitas, dan visualisasi data log yang lebih mudah dimengerti pengguna. Perangkat lunak ELK (Elasticsearch, Logstash, Kibana) bisa dikombinasikan dan diintegrasikan dalam server web untuk mengelola, manampilkan, dan mengawasi data-data log yang ada sehingga administrator jaringan dapat mengetahui kinerja yang terdapat pada server web. Implementasi dan uji coba menunjukkan pengawasan kinerja server web Apache yang lebih mudah dimengerti pengguna.
Model Inferensi Konteks Internet of Things pada Sistem Pertanian Cerdas Ventje Jeremias Lewi Engel; Dina Angela; Sinung Suakanto; Maclaurin Hutagalung
Jurnal Telematika Vol 11, No 2 (2016)
Publisher : Institut Teknologi Harapan Bangsa

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Abstract

Sebagian pertanian di dunia sudah mulai memanfaatkan teknologi informasi dan jaringan sensor untuk membantu pengelolaan lahan pertanian. Sistem ini biasa disebut sistem pertanian cerdas. Implementasi sistem pertanian cerdas yang sedikit melibatkan para pemangku kepentingan bidang pertanian membuat implementasi sensor kurang bisa mendukung dalam pembuatan keputusan. Teknik inferensi konteks dapat membantu mengatasi celah permasalahan tersebut. Penelitian ini menyajikan pemodelan inferensi konteks untuk sistem pertanian cerdas yang memperhatikan: (1) jumlah data yang akan diproses, dan (2) pengiriman data dari lahan ke gateway yang tidak selalu aktif. Model inferensi konteks yang diperlukan adalah yang mudah dibuat, mudah dikonfigurasi, dan cepat diproses serta mendukung lintas lingkungan operasi. Penelitian ini menghasilkan model inferensi konteks dan strategi implementasinya untuk penelitian lanjutan.Most farms have started to use information technology and sensor networks to manage farming field. This system is called smart farming. Smart farming implementation which less involving stakeholders in agriculture field makes the technology not really able to support decision making. Context inference techniques can help with that gap problems. This research presents context inference modeling which considers: (1) the amount of data transfer, and (2) periodic data transmisions. The model designed has to be easy to configure, fast to process, and interoperability. This research had resulted context inference model and its implementation strategy.