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All Journal Journal of Telematics and Informatics POSITIF Faktor Exacta Matrix : Jurnal Manajemen Teknologi dan Informatika JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Telematika Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT
Kurniawan, Ade
Institut Teknologi Sains Bandung
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences Other

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Journal : Jurnal Telematika

 1 
Sejarah, Teori Dasar dan Penerapan Reinforcement Learning: Sebuah Tinjauan Pustaka Andreanus, Jeky; Kurniawan, Ade
Jurnal Telematika Vol. 12 No. 2 (2017)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v12i2.193

Abstract

Today's research on the topic of Machine learning has increased sharply. Machine learning is the future of the world, in the future it will be a revolution in every computer-bound science. This paper examines the field of Reinforcement learning from a computer science perspective. Reinforcement learning is part of Machine learning. In general machine learning is divided into three categories, namely supervised learning, unsupervised learning, and reinforcement learning. Supervised learning requires labeled data to analyze data, training and make conclusions, which can be used for mapping new values. Otherwise, Unsupervised learning does not use labeled data, which is more suitable for relatively irregular problems. In contrast to Reinforcement learning that is based on trial and error, by experimenting on the environment then get a response that will improve its ability. This work is summarized based on the history of Reinforcement learning and the selection of current research. This paper discusses central issues in reinforcement learning, from history, Reinforcement learning models, multiinform Reinforcement learning, including comparison of exploration and exploitation. Ends with a Reinforcement learning implementation survey of several systems. Reinforcement learning is the most suitable Machine learning in learning new things from scratch without human intervention in learning, most of Reinforcement learning is used for in-game learning. But the learning may  takes a long time and is uncertain.Dewasa ini penelitian mengenai topik Machine learning telah meningkat tajam.  Machine learning adalah masa depan dunia, kedepannya ini akan menjadi revolusi dalam segalah ilmu yang terikat dengan komputerisasi. Paper ini meneliti bidang Reinforcement learning dari perspektif ilmu komputer. Reinforcement learning merupakan bagian dari Machine learning. Secara umum machine learning dibagi menjadi tiga kategori, yaitu supervised learning, unsupervised learning, dan reinforcement learning. Supervised learning memerlukan data berlabel untuk menganalisis data, pelatihan dan membuat kesimpulan, yang dapat digunakan untuk pemetaan nilai-nilai baru. Sebaliknya, Unsupervised learning tidak memenggunakan data berlabel, yang mana lebih cocok untuk masalah yang relatif tidak beraturan. Berbeda dengan Reinforcement learning yang berbasis trial and error, dengan mencoba-coba pada lingkungannya kemudian mendapatkan respon yang akan meningkatkan kemampuannya. Karya ini dirangkum berdasarkan sejarah bidang Reinforcement learning dan pemilihan riset saat ini. Paper ini membahas isu-isu sentral dalam reinforcement learning, mulai dari sejarah, model Reinforcement learning, multiagent Reinforcement learning termasuk melakukan perbandingan dari eksplorasi dan eksploitasi. Diakhiri dengan survei penerapan Reinforcement Learning terhadap beberapa sistem. Reinforcement learning merupakan Machine learning yang paling cocok dalam mempelajari hal baru dari nol tanpa campur tangan manusia dalam pembelajarannya, kebanyakan dari Reinforcement learning digunakan untuk belajar dalam game. Namun pembelajaran yang dilakukan membutuhkan waktu lama dan tidak pasti.
Penerapan Framework OWASP dan Network Forensics untuk Analisis, Deteksi, dan Pencegahan Serangan Injeksi di Sisi Host-Based Kurniawan, Ade
Jurnal Telematika Vol. 14 No. 1 (2019)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v14i1.267

Abstract

The Internet has changed the world. The penetration of internet users in 1995 is only 1 percent of the world population, while in 2018, the figure reached 70 percent or 4.5 billion users. Simultaneously, it was reported that eight of the top ten web sites in the world were at a critical point of vulnerability to attacks by injection methods, such as Cross-Site Scripting (XSS) and Structured Query Language Injection (SQLi). Furthermore, XSS and SQLi attacks can be used by certain parties to steal information or specific purposes. In this paper, we research by conducting attack simulations, analyzing packet data, and finally conducting prevention at host-based. Initial simulations of attacks using social engineering attack techniques by sending a phishing email. At this stage of attack simulation, the attack includes information gathering, webcam screenshots, keyloggers, and spoofers. Furthermore, at the stage of analysis, we do with the approach of network forensics with evidence collection techniques using live forensics acquisition. The final stage is prevent (patching) by creating an application or add-on on the browser side by name, XSSFilterAde. This research contribution offers a broad and in-depth study of how to do a simulation, analysis, and finally prevent. Furthermore, the method of protecting the user or host- based solution in the browser application functions to filter, disable plugins, notify, block, and reduce injection attacks.Internet telah mengubah dunia. Internet telah mengubah wajah dunia. Penetrasi pengguna internet di tahun 1995 hanya 1 persen dari populasi dunia, sedangkan di tahun 2018 angkanya telah mencapai 60 persen atau 4,5 miliar pengguna. Secara bersamaan, dilaporkan delapan dari sepuluh situs web teratas di dunia berada pada titik kritis kerentanan terhadap serangan dengan metode injeksi, seperti: Cross-Site Scripting (XSS) dan Structured Query Language Injection (SQLi). Selanjutnya, serangan XSS dan SQLi dapat digunakan oleh pihak tertentu untuk mencuri informasi atau untuk tujuan tertentu. Dalam makalah ini, penelitian dilakukan denganmensimulasikan serangan, analisis paket data, dan terakhir melakukan pencegahan di host-based atau di sisi pengguna. Simulasi awal serangan menggunakan social engineering attack dengan cara mengirim sebuah phishing email. Pada tahapan simulasi serangan ini, serangan meliputi pengumpulan informasi, screenshot webcam, keyloggers, dan spoofer. Selanjutnya, di tahapan analisis, kami melakukan pendekatan network forensics dengan teknik pengambilan barang bukti menggunakan metode live forensics acquisition. Tahapan terakhir adalah mencegah (menambal) dengan membuat sebuah aplikasi atau add-on di sisi browser dengan nama XSSFilterAde. Kontribusi penelitian ini menawarkan sebuah studi secara luas dan mendalam tentang bagaimana melakukan sebuah simulasi,analisis, dan, terahir, melakukan pencegahan (prevent). Lebih jauh, metode solusi perlindungan kepada pengguna atau host-based dalam aplikasi browser berfungsi untuk memfilter, menonaktifkan plugin, memberi tahu, memblokir, dan mengurangi serangan injeksi.
Analisis Unauthorized Access Point Menggunakan Teknik Network Forensics Paramita, Felicia; Madeline, Madeline; Alvina, Olga; Sentia, Rahel Esther; Kurniawan, Ade
Jurnal Telematika Vol. 14 No. 2 (2019)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v14i2.287

Abstract

In this era, free access points are found available in various places. But this freedom comes with a price, and only a few users really understand the risk. In a recent survey, 70% of tablet owners and 53% of smartphone owners stated that they use public wifi hotspots. The biggest threat to public wifi security is how a hacker positions himself as a liaison between victims and Authorized Access Points. To do this the hacker creates an Unauthorized Access Point (Fake Access Point). We took a pentester/attacker POV in this artikel for educational purposes, so that users may know the stages of Fake Access Point attack based on Kali Linux, Fluxion. For the digital evidence analysis stage, we used the customized OSCAR (Obtain information, Strategies, Collect Evidence, Analyze and Report) methods, where attacking is the stage for preparation, determining which wifi Access Points is going to be the target of the attack, and carrying out attacks. While, analysis is the stage of analyzing the steps of attack and how to distinguish between AAP and UAP. The results of our research are that after determining the target, the pen tester/attacker will use aircrack-ng on Fluxion to get a handshake, create a fake web interface, then launch a deauth all attack, also known as DoS, to AAP so that the victim / client cannot connect with the AAP and switch to Fake Access Point. The fake web interface will then ask the victim to enter the password, where after the password is found, the pen tester/attacker can see it through Fluxion. As a precautionary measure, the difference between a Fake Access Point and an Authorized Access Point is found in the presence or absence of the padlock symbol (Android) or an exclamation point (Windows 10).Pada zaman ini, free access point telah tersedia di berbagai tempat. Namun, nyatanya kebebasan ini memiliki harga, dan hanya sedikit pengguna yang memahami benar risikonya. Ancaman terbesar terhadap kemanan wifi publik adalah bagaimana seorang hacker memposisikan dirinya sebagai penghubung antar korban dan Authorized Access Point. Untuk melakukan hal tersebut, hacker membuat Unauthorized Access Point (Fake Access Point). Dalam artikel ini pen tester/attacker diambil sudut pandang sebagai dengan tujuan edukasi, agar pengguna mengetahui tahapan serangan Fake Access Point dengan tool Fluxion berbasis OS Kali Linux. Tahapan analisis bukti digital menggunakan metode OSCAR (Obtain Information, Strategies, Collect Evidence, Analyze and Report) yang telah di kostumisasi, di mana attacking adalah tahapan untuk persiapan menentukan target wifi Access Point yang akan diserang serta menjalankan serangan. Analysis adalah tahapan menganalisa langkah penyerangan serta bagaimana cara membedakan Authorized Access Point dengan Unauthorized Access Point. Hasil penelitian yang dilakukan setelah menentukan target, pentester/attacker akan menggunakan Aircrack-ng pada Fluxion untuk mendapatkan handshake, membuat web interface palsu, kemudian melancarkan serangan Deauth all, dikenal DoS ke AAP, sehingga korban/client tidak dapat terkoneksi dan masuk ke Fake Access Point. Web interface palsu kemudian akan meminta korban untuk memasukkan password. Setelah password ditemukan, maka pen tester/attacker dapat melihatnya melalui Fluxion. Sebagai langkah pencegahan, perbedaan antara Fake Access Point dan yang Authorized Access Point ditemukan pada ada tidaknya simbol gembok (Android) atau tanda seru (Windows 10).
Co-Authors Agung Bara Muli Alvina, Olga Andreanus, Jeky Aprilia, Ely Aulia, Achmad Indra Cristina Cristina Erma Yunita Felicia Paramita Goeirmanto, Leonard Lika, Sudiharyanto Madeline, Madeline Meilvin Wijaya Pernando, Yonky Raymond Erz Saragih Sentia, Rahel Esther Siregar, Amril Mutoi Suhardi Suhardi Verdian, Ihsan Wilianto Wilianto Winda Andrini Wulandari