Assessing Deep Neural Network and Shallow for Network Intrusion Detection Systems in Cyber Security

2021 ◽  
pp. 703-713
Author(s):  
Deena Babu Mandru ◽  
M. Aruna Safali ◽  
N. Raghavendra Sai ◽  
G. Sai Chaitanya Kumar
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Deep Neural Network ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems

2014 ◽  
Vol 5 (3) ◽  
pp. 24-36 ◽  
Author(s):  
Loye Lynn Ray ◽  
Henry Felch
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convergence Rates ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Review Of The Literature ◽  
Feed Forward Neural Network ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems

Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.

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