Zamansal Evrişimli Ağlarla Saldırı Tespiti: Karşılaştırmalı Bir Analiz

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10.31590-ejosat.848784-1474355.pdf

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2021-01-01

Author

ÇAKIR, BERNA
Angın, Pelin

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Son yıllarda Nesnelerin İnterneti paradigmasının hızlı yükselişi ve bu yükselişin yarattığı büyük siber saldırı yüzeyi, otomatik saldırı tespit sistemlerinin önemini arttırmıştır. Özellikle daha önce gözlenmemiş sıfırıncı gün saldırılarının tespitinde klasik imza tabanlı saldırı tespit sistemleri yetersiz kalmaktadır. Bu durum siber güvenlik araştırmacılarını özellikle anomali tespiti için makine öğrenme tabanlı yöntemlere yönlendirmiştir. Literatürde derin öğrenme yöntemlerini bilgisayar ağlarında saldırı tespiti için kullanan birçok yöntem önerilmiş ve yüksek başarım elde etmiştir. Yakın zamanda ilk olarak videolarda aksiyon segmentasyonu için önerilen zamansal evrişimsel ağlar (TCN), zaman serisi içeren öğrenme görevlerinde yüksek başarı elde ettiği halde, bilgisayar ağlarında saldırı tespiti alanındaki etkinlikleri detaylı analiz edilmemiştir. Bu çalışmada TCN’nin saldırı tespiti konusunda başarımı irdelenmiştir. TCN’nin hem ikili sınıflandırma hem de anomali tespiti problemlerindeki başarımı, birçok saldırı tespiti probleminde yüksek başarım elde etmiş tekrarlayan sinir ağları ve tam bağlı sinir ağları yöntemleriyle kıyaslanmıştır. Elde edilen sonuçlar TCN’nin yüksek doğruluklu saldırı tespiti için ümit vaat eden bir yöntem olduğunu göstermektedir.

Subject Keywords

Deep neural networks, Temporal convolutional networks, Attack detection, saldırı tespiti, zamansal evrişimli ağlar, derin sinir ağları

URI

http://dx.doi.org/10.31590/ejosat.848784
https://hdl.handle.net/11511/97084

Journal

European Journal of Science and Technology

DOI

https://doi.org/10.31590/ejosat.848784

Collections

Department of Computer Engineering, Article

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Citation Formats

B. ÇAKIR and P. Angın, “Zamansal Evrişimli Ağlarla Saldırı Tespiti: Karşılaştırmalı Bir Analiz,” European Journal of Science and Technology, no. 22, pp. 204–211, 2021, Accessed: 00, 2022. [Online]. Available: http://dx.doi.org/10.31590/ejosat.848784.