A Survey on Intrusion Detection System for Software Defined Networks (SDN)

HandeYogita,; Muddana, Akkalakshmi

doi:10.4018/ijbdcn.2020010103

Cited by 25 publications

(9 citation statements)

References 56 publications

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“…Software defined network intrusion (SDNI) mengelola dan mengkonfigurasi jaringan melalui abstraksi tingkat tinggi. Sifat SDNI yang dinamis dan dapat diprogram menghadapi banyak masalah keamanan yang menuntut solusi keamanan yang inovatif [2].…”

Section: Pendahuluanunclassified

“…SDN menggunakan berbagai antarmuka, seperti antarmuka Northbound dan Southbound, untuk komunikasi antar pesawat. Antarmuka Northbound (NBI) digunakan antara lapisan aplikasi dan lapisan kontrol, sedangkan Antarmuka Southbound (SBI) memungkinkan lapisan kontrol untuk berkomunikasi dengan lapisan data [2].…”

Section: Pendahuluanunclassified

“…Pada penelitian dengan judul A Survey on Intrusion Detection System for Software defined network intrusions (SDNI), tujuan dari penelitian ini adalah Software defined network intrusion (SDNI) suatu bentuk sistem keamanan yang terbaru yang dapat di program dan terpusat guna menghadapi serangan cyber dan keamanan yang menuntut mekanisme keamanan inovatif seperti sistem deteksi intrusion (IDS) [2].…”

Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

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Analisa Dataset Software Defined Network Intrusion Menggunakan Algoritma Deep Learning H2o

Rizal¹,

Martanto²,

Wijaya³

2022

jati

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Software-defined networking Intrusion (SDNI) baru-baru ini menjadi salah satu solusi paling menjanjikan untuk Internet masa depan. Dengan sentralisasi logis dari pengontrol dan tampilan jaringan global, SDN Intrusion menawarkan peluang untuk meningkatkan keamanan jaringan. Pada penelitian sebelumnya oleh Omar Jamal Ibrahim, dan Wesam S. Bhaya menjelaskan tentang dataset SDN intrusion bahwa dengan menggunakan algoritma Support Vector Machine (SVM) diperoleh dengan nilai akurasi sebesar 97.77%, sehingga menurut peneliti ini masih bisa untuk di kaji lagi dengan menggunakan algortima yang berbeda. Sebagai proses pencarian informasi dari sekumpulan data yang akan dijadikan pengetahuan baru dapat dimanfaatkan maka dari itu data mining juga seringkali dikenal dengan sebutan Knowledge Discovery in Database (KDD). Metode klasifikasi yang digunakan yaitu Deep Learning H2O yaitu suatu metode menggunakan algoritma multilayer yang di sebut neural networks. Tujuan dari algoritma ini mencoba untuk mengambil suatu kesimpulan berdasarkan struktur logika yang di berikan secara berkelanjutan. Peneliti menggunakan software aplikasi Rapid Miner sebagai bantuan dalam menganalisis dataset. Dari hasil penelitian terbukti bahwa algoritma Deep Learning H2O yang digunakan lebih baik. Hal ini dibuktikan dengan hasil evaluasi penelitian bahwa algoritma Deep Learning H2O mampu menganalisa nilai recall 100.00% dan tingkat akurasi sebesar 99.66% sehingga model klasifikasi menggunakan algoritma Deep Learning H2O lebih baik saat diterapkan pada dataset yang digunakan

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Section: Pendahuluanunclassified

Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

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Analisa Dataset Software Defined Network Intrusion Menggunakan Algoritma Deep Learning H2o

Rizal¹,

Martanto²,

Wijaya³

2022

jati

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“…A typical NBI example is the REST API, while the SBI is operated under the open ow protocol standard to implement on hardware and software environments to implement the SDN [1], [6]- [7]. The controller in SDN is an essential component that serves as intelligence for the whole network [8]. SDN networks with software switches [9] are implemented using a variety of modeling and emulation platforms to virtualize the hardware needs.…”

Section: Introductionmentioning

confidence: 99%

Detection and Mitigation of Malicious DDoS Floods in Software Defined Networks

Ahmad

Rehman

Saleem

2023

Preprint

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Software-defined networking (SDN) has revolutionized network management by providing modular control and data plane attributes for flexible network management. It implies the concept of separating the control and data plane attributes for flexible network management. However, centralized management due to control plane separation in SDN also exposes it to cyber threats such as Distributed Denial-of-service (DDoS) attacks that can compromise the SDN controllers. In recent research, entropy-based attack detection approaches showed much significance among other detection methods but relying on entropy itself can neglect detection in several variables such as variations in flow specification. Based on these limitations, in this work, we have designed a DDoS attack detection framework inside the SDN control plane by integrating the packet flow initiation and its specifications properties with entropy-based algorithm to ensure correct measures of attack detection. The simulation is performed on Mininet network simulator, for implementing SDN architecture and the testbed is created on UDP flood attacks on commonly used data-centric tree topologies. Based on experimentation, this lightweight framework is designed to mitigate DDoS attacks by detecting its effects in the early stages to prevent SDN controller being hijacked due to immense packet flooding Based on the results, the proposed solution assures the SDN-based DDoS attack detection and mitigation under 150 packets maintaining significantly low detection time and high accuracy.accuracy.

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“…This property of SDN also puts it forward as a substitute for efficient middleware in IoT networks. In recent advancements, machine learning and artificial intelligence (AI) have been used with SDN for quick and effective intrusion detection in 5G networks [103][104][105][106]. The SDN controllers can be utilized to create smart networks with AI techniques for enhanced security and network management [107].…”

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confidence: 99%

Security in Low Powered Wide Area Networks: Opportunities for Software Defined Network-Supported Solutions

2020

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The Internet of things (IoT) has revolutionized the use of connectivity and has given birth to new transmission technologies to satisfy the requirements of diverse IoT applications. Low powered wide area networks (LPWAN) is one of those transmission technologies, and is becoming exceptionally useful for IoT applications. The nodes use energy-efficient mechanisms for long-range data transmission (10–20 km), lasting in hostile environments for years and making them suitable for IoT applications such as environmental monitoring, automated billing systems, smart homes, smart offices, and patient monitoring. However, LPWAN devices have minimal resources, which makes it challenging to provide promising security to devices and data in the network. In this paper, we discuss the security mechanisms used in current LPWAN technologies along with their vulnerabilities and possible attacks on them. A detailed literature review is conducted on existing solutions on the security of constrained IoT networks similar to LPWAN using different networking frameworks. The reviewed literature is then compared based on various network security measures addressed by them. In addition, the emergence of software defined network (SDN) architecture for security in IoT is explained based on literature. Finally, the applicability of SDN in LPWAN security, its opportunities, and challenges in implementation are discussed.

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A Survey on Intrusion Detection System for Software Defined Networks (SDN)

Cited by 25 publications

References 56 publications

Analisa Dataset Software Defined Network Intrusion Menggunakan Algoritma Deep Learning H2o

Analisa Dataset Software Defined Network Intrusion Menggunakan Algoritma Deep Learning H2o

Detection and Mitigation of Malicious DDoS Floods in Software Defined Networks

Security in Low Powered Wide Area Networks: Opportunities for Software Defined Network-Supported Solutions

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