Archive for December, 2020

Performance Analysis of RPL Protocols in LLN Network Using Friedman’s Test

December 16, 2020


This paper provides a comparison study of the quality services of RPL protocols in low-power and lossy networks (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called RPL-OC, with the standard and other RPL variants. OC based approach is applied to extract the feasible end-to-end paths while assigning a rank to each one. The goal is to provide a tuple that containing the most efficient paths in end-to-end manner by considering more network metrics instead of one. Further, to address some significant issues of the performance analysis, a statistical test has been performed in order to determine whether the proposed protocol outperforms others or not by using Friedman test. The results show that there is a strong indication that four different protocols were analyzed and compared. It reveals that the proposed scheme outperforms others, especially in terms of end-to-end delay and energy consumption which allow ensuring quality of services requirements for Internet of Things (IoT) or smart city applications.

Index Terms—RPL, Routing, LLN, IoT, Friedman Test.

This paper has been already accepted on the Symposium on Solutions for Smart Cities Challenges 2020 (SSCC 2020) in conjunction with The Seventh International Conference on Internet of Things: Systems, Management and Security (IoTSMS 2020), Paris, France, December 14-16, 2020. Accepted papers will appear in the IoTSMS Proceeding and to be submitted to IEEE Xplore for inclusion. And in addition, this paper is one of the Workshops Best Paper Nominees. However, due to pandemic Covid-19, the conference is transformed into an online virtual conference.


Screenshot of Workshops Best Paper Nominees (Taken from the Opening Slides of keynote speaker)

Screenshot from the virtual conference.

Colmar, 16 December 2020

Pendeteksi Penipuan (Fraud) Kartu Kredit Berbasis Kecerdasan Buatan (Artificial Intelligence)

December 7, 2020


Kejahatan (fraud/scam) kartu kredit masih terus terjadi dan angka kasus penipuan masih terus meningkat dari tahun ke tahun. Hal ini telah menjadi permasalahan internasional hingga saat ini, termasuk Indonesia dengan jumlah kasus yang cukup tinggi. Berdasarkan data yang didapat dari berbagai kasus kejahatan perbankan yang terjadi di Indonesia pada tahun 2017 – 2018, ditemukan bahwa hampir 50 % kasus kejahatan perbankan terjadi pada bank pemerintah dan 80 % pelaku kejahatan perbankan adalah di tingkat manajemen. Belum lagi ditambah kasus kejahatan dari pihak lain, seperti pencurian kartu kredit/debit, pengambilalihan akun (account takeover), kartu tiruan (counterfeit cards), penipuan dalam pengajuan kartu (fraudulent application), cetakan berkali-kali (multiple imprints), dan kejahatan melalui pemesanan surat, telepon maupun e-commerce (Mail order, telephone order or e-commerce fraud).
Pengawasan pada tingkat internal perbankan yang masih lemah serta kepercayaan nasabah kepada perbankan, dijadikan kesempatan untuk melancarkan aksi kejahatan oleh para pelaku.
Diperlukan waktu beberapa bulan untuk dapat mendeteksi aksi kejahatan (fraud/scam).
Pada propsoal ini, kami menawarkan sebuah sistem yang mampu mendeteksi apakah sebuah transaksi perbankan itu sah (legit) atau tidak sah (fraud) secara real-time dengan menggunakan sebuah kecerdasan buatan (artificial intelligence) dan machine learning.

Kata Kunci: Fraud, deteksi, Penipuan, Scam, Kartu Kredit, Machine Learning, Artificial Intelligence, Kecerdasan Buatan

Colmar, 7 Desember 2020