• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Digital Library


Search: "[ keyword: NOMA ]" (19)
  1. 11. Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder
    Byeoungjun Min, Jihoon Yoo, Sangsoo Kim, Dongil Shin, Dongkyoo Shin, Vol. 22, No. 1, pp. 13-22, Feb. 2021
    10.7472/jksii.2021.22.1.13
    Keywords: Anomaly Detection, Network intrusion detection, Autoencoder, NSL-KDD
  2. 12. Deep Learning-based Abnormal Behavior Detection System for Dementia Patients
    Kookjin Kim, Seungjin Lee, Sungjoong Kim, Jaegeun Kim, Dongil Shin, Dong-kyoo shin, Vol. 21, No. 3, pp. 133-144, Jun. 2020
    10.7472/jksii.2020.21.3.133
    Keywords: Abnomaly detection, deep-learning, Autoencoder, Long Short-Term Memory models
  3. 13. Unstructured Data Analysis using Equipment Check Ledger: A Case Study in Telecom Domain
    Yeonjin Ju, Yoosin Kim, Seung Ryul Jeong, Vol. 21, No. 1, pp. 127-135, Feb. 2020
    10.7472/jksii.2020.21.1.127
    Keywords: Equipment Ledger, Unstructured Data, Telecom Domain, Anomaly Detection
  4. 14. Minimized Transmit Power Full Duplex NOMA Relay System for 5G Wireless Networks
    Kyuha Kim, Wonsuk Yoo, Jong-Moon Chung, Vol. 20, No. 3, pp. 13-24, Jun. 2019
    10.7472/jksii.2019.20.3.13
    Keywords: NOMA, Full Duplex, 5G, Minimum Energy Consumption
  5. 15. DDoS Attack Analysis Using the Improved ATMSim
    Hae-Duck J. Jeong, Myeong-Un Ryu, Min-Jun Ji, You-Been Cho, Sang-Kug Ye, Jong-Suk R. Lee, Vol. 17, No. 2, pp. 19-28, Apr. 2016
    10.7472/jksii.2016.17.2.19
    Keywords: Anomaly traffic, self-similarity, Hurst parameter, ATMSim, DDoS attack
  6. 16. Design and Evaluation of an Anomaly Detection Method based on Cross-Feature Analysis using Rough Sets for MANETs
  7. 17. Anomaly Detection Mechanism against DDoS on BcN
  8. 18. Anomaly Detection Performance Analysis of Neural Networks using Soundex Algorithm and N-gram Techniques based on System Calls
  9. 19. Network based Anomaly Intrusion Detection using Bayesian Network Techniques