scholarly journals Deteksi Serangan SQL Injection Menggunakan Hidden Markov Model

2021 ◽  
Vol 5 (2) ◽  
pp. 243
Author(s):  
Pramono * ◽  
Andi Sunyoto ◽  
Eko Pramono
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Query Language ◽  
Hidden Markov ◽  
Web Security ◽  
Sql Injection ◽  
Structured Query Language

Serangan aplikasi web terus meningkat jumlahnya dan dalam tingkat keparahan. Information besar tersedia di internet memotivasi penyerang untuk melakukan serangan jenis baru. Di dalam konteks, penelitian intensif tentang keamanan aplikasi web telah dilakukan. Serangan berbahaya yang menargetkan web aplikasi adalah Structured Query Language Injection (SQLI). Serangan ini merupakan ancaman serius bagi web aplikasi. Beberapa pekerjaan penelitian melakukan cara untuk mengurangi serangan ini baik dengan mencegahnya dari awal tahap atau mendeteksinya saat itu terjadi. Dalam tulisan ini, kami sajikan gambaran umum tentang serangan injeksi SQL dan klasifikasi dari solusi deteksi dan pencegahan yang baru diusulkan. Dalam penelitian ini kami menggunakan Hidden Markov Model (HMM) untuk melakukan metode deteksi dan pencegahan dari serangan SQLI untuk mengurangi serangan ini khususnya yang didasarkan pada ontologi dan pembelajaran mesin. Kata kunci: HMM, SQL Injection, Web Security

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.

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