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Deep Stacking Network for Intrusion Detection [PDF]
Sensors, 2021 Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems.
Yifan Tang, Lize Gu, Leiting Wang
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Oblivious network intrusion detection systems. [PDF]
Sci Rep, 2023 Abstract A main function of network intrusion detection systems (NIDSs) is to monitor network traffic and match it against rules. Oblivious NIDSs (O-NIDS) perform the same tasks of NIDSs but they use encrypted rules and produce encrypted results without being able to decrypt the rules or the results.
Sayed MA, Taha M.
europepmc +4 more sources
Few-Shot network intrusion detection based on prototypical capsule network with attention mechanism [PDF]
PLoS ONE, 2023 Network intrusion detection plays a crucial role in ensuring network security by distinguishing malicious attacks from normal network traffic. However, imbalanced data affects the performance of intrusion detection system.
Handi Sun +3 more
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Analysis of Autoencoders for Network Intrusion Detection. [PDF]
Sensors (Basel), 2021 As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks ...
Song Y, Hyun S, Cheong YG.
europepmc +5 more sources
ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection [PDF]
SensorsNetwork intrusion detection systems can identify intrusion behavior in a network by analyzing network traffic data. It is challenging to detect a very small proportion of intrusion data from massive network traffic and identify the attack class in ...
Bin Li, Jie Li, Mingyu Jia
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Wireless Network Intrusion Detection Algorithm Based on Multiple Perspectives Hierarchical Clustering [PDF]
Jisuanji kexue yu tansuo, 2022 Aiming at the problems of high false detection rate, difficult to find unknown attack behavior and high cost of obtaining marked data in existing wireless network intrusion detection algorithms based on supervised learning, this paper proposes an ...
DONG Xinyu, XIE Bin, ZHAO Xusheng, GAO Xinbao
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Enhanced Network Intrusion Detection System [PDF]
Sensors, 2021 A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15
Ketan Kotecha +8 more
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Intrusion Detection Method Based on Denoising Autoencoder and Three-way Decisions [PDF]
Jisuanji kexue, 2021 Intrusion detection plays a vital role in computer network security.Intrusion detection is one of the key technologies of network security and needs to be kept under constant attention.As the network environment becomes more and more complex,network ...
ZHANG Shi-peng, LI Yong-zhong
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IEEE Access, 2023 Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years.
Li Zou +4 more
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Network Intrusion Detection [PDF]
, 2011 Attacks against computers and the Internet are in the news every week. These primarily take the form of malicious code such as viruses and worms, or denial of service attacks. Less commonly reported are attacks which gain access to computers, either for the purpose of producing damage (such as defacing web sites or deleting data) or for the ...
Yong Shi +4 more
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