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Journals / Balkan Journal of Electrical and Computer Engineering / 2021 / Cilt: 9 Sayı: 4

Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks

Ebru Efeoğlu(TRAKYA UNIVERSITY)0000-0001-5444-6647
Gürkan Tuna(TRAKYA UNIVERSITY)0000-0002-6466-4696
Pages
342–347
Publication Date
2021-10-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack.
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Key Findings & Data:Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics.
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Conclusion & Impact:As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Burst header packet flooding · Classification algorithms · Optical burst switching

Abstract

Optical burst switching networks are vulnerable to various threats including Burst Header Packet Flooding attack, Circulating Burst Header attack, Address Spoofing, and Replay attack. Therefore, detecting such threats play a key role in taking appropriate security measures. One of the major challenges in identifying the risks of Burst Header Packet flooding attacks is the lack or insufficiency of reliable historical data. In this paper, firstly, Burst Header Packet flooding attacks are classified into four categories, Misbehaving-Block, Behaving-No Block, Misbehaving-No Block and Misbehaving-Wait, using Naive Bayes and K-Nearest Neighbor algorithms. Using performance metrics obtained both after testing on the same set and after applying 10-fold cross validation, the performance of Naive Bayes and K-Nearest Neighbor algorithms is compared based on commonly used performance metrics. As the results show, compared to Naive Bayes, K-Nearest Neighbor algorithm is more suitable for predicting Burst Header Packet Flooding attacks.

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Cite This Article

Efeoğlu, E., Tuna, G. (2021). Performance Evaluation of Sequential Minimal Optimization and K* Algorithms for Predicting Burst Header Packet Flooding Attacks on Optical Burst Switching Networks. Balkan Journal of Electrical and Computer Engineering, Cilt: 9 Sayı: 4, 342–347. https://doi.org/10.17694/bajece.892150

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