A novel intelligent-based intrusion detection system approach using deep multilayer classification
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Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technology advances, making it difficult to detect an incursion. Detecting and mitigating these threats is a significant issue for standard analytic methods. Furthermore, the attackers use complex processes to remain undetected for an extended period. The changing nature and many cyberattacks require a quick, adaptable, and scalable defense system. For the most part, traditional machine learning-based intrusion detection relies on only one algorithm to identify intrusions, which has a low detection rate and cannot handle large amounts of data. To enhance the performance of intrusion detection systems, a new deep multilayer classification approach is developed. This approach comprises five modules: preprocessing, autoencoding, database, classification, and feedback. The classification module uses an autoencoder to decrease the number of dimensions in a reconstruction feature. Our method was tested against a benchmark dataset, NSL-KDD. Compared to other state-of-the-art intrusion detection systems, our methodology has a 96.7% accuracy.
Açıklama
Anahtar Kelimeler
Network, Internet, Things
Kaynak
Mathematical Problems in Engineering
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
2022
Sayı
Künye
Ugendhar, A., Illuri, B., Vulapula, S. R., Radha, M., Alenezi, F., Althubiti, S. A., & Polat, K. (2022). A novel intelligent-based intrusion detection system approach using deep multilayer classification. Mathematical Problems in Engineering, 2022.
