Network security prediction model using neural networks

Abstract

The aim of this paper is to design a robust network security prediction model. The designed model is an intrusion detecting model constructed using neural networks. The intrusion detecting model detects anomaly and misuse-based attacks. Intrusion detecting model also performs three kinds of classification tasks. The tasks include classifying between occurrence of an attack or a normal case, classifying between different attack types or a normal case and classifying between occurrence of a attack or other case. The intrusion detecting model also shows the classification accuracy, execution time and amount of memory usage. The objectives of the intrusion detecting model are high accuracy, low execution time and minimum amount of memory usage. The intrusion detecting model constructed using neural networks, fulfils the objectives of high accuracy, low execution time and minimum amount of memory usage.

Modeling and Analysis of Network Direct Reporting Security of Infectious Diseases based on Wireless Communication Technology

“…Node S compares data M and data M1. If they are the same, node T successfully forwards the data [15] . If node S does not listen to the data forwarded by node T, or data M is different from data M1, the malicious behavior of node T will be recorded.…”

Prediction of network security situation awareness based on an improved model combined with neural network

“…Network attacks are becoming more complex and diverse. Although the current network security technologies, such as firewall, 3 intrusion detection, 4 and virtual private network (VPN), 5 play a role in network security, they all have defects and are not suitable for the current more and more complex network environment; therefore, network security situation awareness (NSSA) appears 6 . NSSA evaluates the current network state by processing the network security data, and on this basis, it can carry out network security situation prediction (NSSP) 7 .…”