چکیده مقاله
This paper delves into examining the utilization of autoencoders in unsupervised deep learning techniques applied to Network Based Anomaly Intrusion Detection Systems IDS Given the inadequacy of anomaly base traditional IDSs in detecting zero day attacks, enhancing their performance in that aspect remains an active research pursuit This study conducts a comprehensive review of two Denoising Autoencoder DAE and sparse autoencoder approaches for identifying novel attacks The models utilizing AE aim to generate distinctive features conducive to detecting network intrusions By considering either the number of citations or the significance of emerging methods, relevant works were identified, thoroughly examined, and summarized The cybersecurity datasets employed in this investigation are publicly accessible and widely recognized Furthermore, the primary focus of this study is on various autoencoder methodologies within self taught learning, serving as an automated means for feature acquisition
کلیدواژهها
نویسندگان
Homa Taherpour Gelsefid
Bachelor Student in Computer Engineering, Faculty of Technology and Engineering, East of Guilan, University ofGuilan, Guilan, Iran
Seyyed Abdorreza Hesam Mohseni
University Lecturer of Computer Engineering, Faculty of Technology and Engineering, East of Guilan, University ofGuilan, Guilan, Iran
شیوه ارجاع
Taherpour Gelsefid, Homa and Hesam Mohseni, Seyyed Abdorreza,1402,Enhancing Network Intrusion Detection Systems Using Unsupervised Deep Learning Approaches with Autoencoders for Anomaly Detection,5th International Conference on Software Computing,Rudsar
ارائهشده در
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