The security of computer networks is critical for network intrusion detection systems (NIDS). However, concerns exist about the suitability and sustainable development of current approaches in light of modern networks. Such concerns are particularly related to increasing levels of human interaction required and decreased detection accuracy. These concerns are also highlighted. This post presents a modern intrusion prevention deep learning methodology. For unattended function instruction, we clarify our proposed Symmetric Deep Autoencoder (SDAE). Also, we are proposing our latest deep research classification model developed with stacked SDAEs. The classification proposed by the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Canadian Institute for Cybersecurity -Intrusion Detection System (CICIDS 2017) data sets was implemented in Tensor Flow, a Graphics Procedure Unit (GPU) enabled and evaluated. We implemented and tested our experiment with different batch sizes using Adam optimizer. Promising findings from our model have been achieved so far, which demonstrates improvements over current solutions and the subsequent improvement for use in advanced NIDS.
Intrusion detection systems (IDSs) play an essential role in defense of all networks and information systems around the world. IDS is one way of reducing malicious attacks. When attackers adjust their attack tactics and find alternative attack strategies, IDS must also develop through more advanced methods. Deep learning is a subfield of machine learning (ML) methods focused on learning results. A comprehensive review of various deep learning methods employed in IDSs is discussed first in this paper. Then a deep classification scheme is introduced, and the significant works recorded in the deep learning works are summarized. We performed a taxonomy survey of the deep architectures and algorithms accessible in these works and grouped such algorithms into three groups: hierarchical, composite, and generative. Afterward, a wide range of intrusion detection fields investigates selected deep learning applications. Finally, we address common types of datasets and frameworks.
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