Abstract
Network intrusion can enter the network through informal channels. Some illegal users utilize Trojans and self-programmed attack to change the network security system, so that the system loses the defense and alarm function and the Hacker can steal the internal information. Network in
Highlights
With the arrival of the era of big data, network information is facing more and more security threats
To ensure that each kind of maximum anomaly intrusion behavior can be detected, this paper proposes a margin distance minimum selective integration (MDMSE) algorithm to order of all sub-learning gain
BL-kernel extreme learning machine (KELM) effectively reduces the number of integrated sub-learners, eliminating the impact of weak learners on the overall integrated learners, because it based on margin distance minimization (MDM) criteria selects KELM sub-learners with good performance to integrate, which improves accuracy rate (AR), reduces missing rate (MR) and detection time
Summary
With the arrival of the era of big data, network information is facing more and more security threats. Traditional intrusion detection [4,5,6] includes two detection types, namely, misuse detection and anomaly detection. Misuse detection finds the abnormal links in the network by establishing an intrusion rule base. The accuracy is high, but it is powerless for the new intrusion types and the old virus variant connection. Detection is used to analyse network anomalies by summarizing the characteristics of normal network connections. Because this method has a good detection effect against the new attacks, it has been widely concerned
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