Ling Zhang
ABSTRACT
The university smart campus network is an open network; campus network bandwidth is generally high Used to meet modern teaching requirements. But the college user group is relatively active, which provides a favorable environment for hackers to invade. At the same time, different subnets such as teaching subnets, student subnets, and administrative subnets in the campus network have different characteristics, which brings great difficulties to the construction of the campus network intrusion detection subsystem. At present, commonly used detection methods for intrusion detection include pattern matching, state transition analysis, statistical analysis, data mining, neural network and other technologies. Based on the neural network algorithm, this paper proposes an improved algorithm based on artificial bee colony, which optimizes the weights and thresholds of the network, thereby improving the self-learning ability of the neural network and accelerating its convergence speed, so that the neural network can be better implemented in intrusion detection to improve detection accuracy.
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Index Terms
- Research on Intrusion Detection Algorithm Based on Smart Campus Network Security
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