Implementation And Analysis NIDS Based on Snort with Fuzzy Method For Overcoming Attack LoRaWAN

  • Della Vinka Sandi Universitas Gadjah Mada
  • Muhammad Arrofiq Universitas Gadjah Mada
##plugins.pubIds.doi.readerDisplayName##: https://doi.org/10.29207/resti.v2i3.504

Abstract

Indonesia is one of agrarian countris which has a fertile soil condition, but the agricultural products nowadays are not maximal in certain areas particularly strawberry plantation. Strawberry plant it self needs precise temperature and humidity level to maximize strawberry harvest. Soil humidity and air temperature are changing many times caused by the weather. Therefore, this research will build a prototype which is called Smart Agriculture for monitoring the temperature and soil humidity in strawberry plantation. Temperature and soil humidity data will be sent through wireless transmission media to smartphone using LoRa and LoRaWAN technology. This technology could send the data in a long distance but it's server is vulnerable to attacks such as flooding payload data from LoRa node, ping of death or ping flooding, and scanning port. This research implements that attack on LoRaWAN network server which influences server bandwidth , delay, jitter, and throughput from normal condition. To detect an attack, Snort NIDS method and attack classification are used with fuzzy logic method. The result of this research are temperature and humidity readings, attack notification, and attacker address blocking. Besides, it has proven that fuzzy and snort can optimize server performance.

 

Downloads

Download data is not yet available.

References

[1] Al-Ali, A., Zualkeman, I. A., Gupta, R., & Karar, M. A. (2017). A Smart Home Energy Management System Using IoT and Big Data Analytics Approach. IEEE Transactions on Consumer Electronics, 63, No.4, 426-434.
[2] Alnabulsi, H., Islam, M. R., & Mamu, Q. (2014). Detecting SQL Injection attacks using SNORT IDS. IEEE.
[3] Alsubhi, K., Boutaba, R., & Al-Shaer, E. (2008). Alert prioritization in Intrusion Detection Systems. IEEE.
[4] Andrei, M. L., Radoi, A., & Tudose, S. (2017). Measurement of Node Mobility for the LoRa Protocol . IEEE.
[5] Budiman, S. A., Iswahyudi , C., & Sholeh, M. (2014). Implementasi Intrusion Detection System (IDS) Menggunakan Jejaring Sosial Sebagai Media Notifikasi. Prosiding Seminar Nasional Aplikasi Sains and Teknologi .
[6] Cox, E. (1994). The Fuzzy System Handbook. Academic Press - Inc.
[7] Dewi, E. K., & Kasih, P. (2017). Analisis Log Snort Menggunakan Network Forensik. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika, vol 2, no 2.
[8] El-Hajj, W., Aloul, F., & Trabelsi, Z. (2008). On Detecting Port Scanning using Fuzzy Based Intrusion Detection System. IEEE.
[9] Elvira, F., Duskarnaen, M. F., Z, B., & Isharyanto, B. (2010). Sistem Keamanan Jaringan Komputer Dengan Menggunakan Firewall Iptables dan Snort. Universitas Negeri Jakarta.
[10] Kontogiannis, S., Kokkonis, G., Ellinidou, S., & Valsamidis, S. (2017). Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems. MDPI, Future Internet.
[11] Kuswardani. (2011). Sistem Deteksi Dan Penanganan Intruisi Menggunakan Snort dan Base Implementasi Pada Pt. Oasys Solusi Teknologi.
[12] Lavric, A., & Popa, V. (2017). LoRa Wide-Area Networks from an Internet of Things Prespective. ECAI.
[13] Nugroho, I. W., Harianto, & Mardiana, I. G. (2014). Rancang Bangun Aplikasi Intrussion Detection System Dengan Menggunakan Metode Fuzzy. stikom, vol 3, no 1.
[14] Sembiring, I., Widiasari, I. R., & Prasetyo , S. D. (2009). Analisa dan Implementasi Sistem Keamanan Jaringan Komputer dengan Iptables sebagai Firewall Menggunakan Metode Port Knocking. Univeritas Kristen Satya Wacana.
[15] Tiyas , F. I., Hadi, M. Z., & K, E. M. (2011). Aplikasi Web untuk Metode Fuzzy Neural Network pada Intrusion Detection System Berbasis Snort. Pens Institut Teknologi Sepuluh Nopember Surabaya.
Published
2018-10-19
Section
Technology Information Article