A novel approach for intrusion detection systems: V-IDS

İNCE K.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, cilt.29, sa.4, ss.1929-1943, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3906/elk-2005-1
  • Dergi Adı: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1929-1943
  • Anahtar Kelimeler: Data visualization, deep learning, intrusion detection, network security, DEEP LEARNING APPROACH, NEURAL-NETWORKS
  • İnönü Üniversitesi Adresli: Evet

Özet

An intrusion detection system (IDS) is a security mechanism that detects abnormal activities in a network. An ideal IDS must detect intrusion attempts and maybe categorize them for further research and keep false-positive analysis at a very low level. IDSs are used in the analysis of network traffic data at all sizes. Studies on this subject focused on machine learning techniques. Even though the performance rates are high, it is seen that processes such as data understanding, preprocessing, and consistency tests are time-consuming and laborious. For this reason, the use of deep learning (DL) models that automatically perform the mentioned steps has become very popular. In this study, a high-performance approach that can be applied in real-time systems is proposed: visual IDS (V-IDS). NSLKDD dataset, one of the large-scale datasets, is used. Data visualization techniques were applied in order to determine geometric relationships between records, and the data were classified by using the DL model. The model achieved 98% accuracy in total and even higher in some intrusion categories.