Deteksi Dini Gangguan Jaringan Distributed Denial Of Service (DDOS) Menggunakan Metode Shannon Entropy Pada Software Defined Network (SDN)

Penulis

  • Achmad Solichin Universitas Budi Luhur, Jakarta
  • Ludi Nugroho Universitas Budi Luhur, Jakarta

DOI:

https://doi.org/10.25126/jtiik.938188

Kata Kunci:

deteksi dini, Software Defined Networking, teori informasi, distributed denial of service, dataset, lalu lintas.

Abstrak

Software Defined Networking (SDN) adalah arsitektur jaringan baru yang memisahkan antara control dan data plane. Aspek keamanan utama dalam control plane salah satunya adalah serangan DoS dan DDoS. Serangan DDoS mengakibatkan terjadinya penurunan performa jaringan yang berjalan sangat lambat. Serangan DDoS dilakukan dengan menyusupi dan membanjiri bandwidth ke sumber daya target, sehingga dapat menyebabkan penolakan layanan bagi pengguna yang mengaksesnya. Tak hanya itu, serangan DDoS menyebabkan penurunan sumber daya jaringan seperti kapasitas memory dan CPU. Akibatnya kerusakan signifikan pada sistem yang menjadi korban serangan dapat mengalami kerugian, baik secara finansial, reputasi bahkan kehilangan pelanggan yang membutuhkan layanan tersebut. Mencegah serangan DDoS diperlukan suatu tindakan pencegahan yaitu dengan deteksi dini serangan DDoS untuk mengurangi dampak serangan dan memulihkan sistem dengan lebih cepat. Deteksi dini yang disebabkan oleh DDoS pada jaringan SDN dilakukan melalui pendekatan metrik entropy berbasis teori informasi. Penelitian ini memfokuskan pendeteksian dini pada serangan DDoS di dalam lingkungan SDN melalui metode Shannon Entropy dengan mendeteksi lalu lintas atau trafik normal dan DDoS. Penelitian ini menggunakan dataset publik dari InSDN yang diterbitkan pada tahun 2020 untuk menentukan nilai ambang batas lalu lintas normal dan DDoS. Hasilnya, penelitian ini berhasil mendeteksi dini lalu lintas normal dan lalu lintas serangan DDoS dengan nilai entropy sesuai ambang batas, dengan nilai akurasi 100%, presisi 100% dan recall 100% yang dihitung menggunakan confusion matrix. Deteksi dini menampilkan akurasi dan performa yang dapat berkontribusi banyak dalam menunjang tingkat keamanan melalui pencegahan tahap awal, sehingga hasilnya dapat meningkatkan keamanan dan efektifitas pada lingkungan SDN.

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Referensi

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Diterbitkan

31-07-2024

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Deteksi Dini Gangguan Jaringan Distributed Denial Of Service (DDOS) Menggunakan Metode Shannon Entropy Pada Software Defined Network (SDN). (2024). Jurnal Teknologi Informasi Dan Ilmu Komputer, 11(3), 461-474. https://doi.org/10.25126/jtiik.938188