Since some security flaws exist in the arp protocol, some network attacks may arise such as arp overflow, arp spoofing and so on. By analyzing the modes of arp attack on campus network, the paper puts forward a defense algorithm against the arp attack. In the algorithm, the arp packets sent and receive by the host computer are to be detected ,and those differing ones are abandoned; In accordance with the policy of “receiving arp response after sending an arp request ”checks the arp response packets, those no-request ones are refused. The above defense algorithm can effectively prevent arp attacks, which can be applied in the campus network with high-safety requirements.

Magnetic ellipsoid tracking problem is characterized by high nonlinearity. In this study, the determination of target position, magnetic moment, and velocity is formulated as a Bayesian estimation problem for dynamic systems, a recursive approach is proposed to estimate the trajectory and magnetic moment component of the target using data collected with a magnetic gradiometer tensor. Particle filter provides a solution to this problem. In addition to the conventional particle filter, the proposed tracking and classification algorithm uses Gaussian mixed mode to represent the posterior state density of the unknown parameters, which is named as Gaussian mixture sigma point particle filters(GMSPPF). The performance of the proposed method has been evaluated through simulation experiment. The results indicate that the method has achieved the magnetic ellipsoid tracking and GMSPPF has better estimation performance and less computational complexity than other related algorithms.