To address the low efficiency and the difficulty of balancing high success rate with motion smoothness in robotic end-to-end dynamic grasping tasks， a robotic end-to-end dynamic grasping method based on Curriculum Reinforcement Learning （CRL） was proposed. First， a multi-modal input network that fuses color images， depth maps， and robot proprioceptive states was constructed to map raw sensory data directly to continuous action commands for the end-effector. Second， a curriculum mechanism with synchronously increasing difficulty and smoothness constraints was designed， and combined with a staged reward function， the agent was guided to master grasping from static to dynamic ones progressively. Finally， Domain Randomization （DR） was employed to enhance the policy's transfer capability of Simulation-to-Reality （Sim-to-Real）. Simulation results show that the proposed method achieves a grasping success rate of nearly 100% at target speeds ranging from 0.15 to 0.40 m/s， elevating the upper speed limit for stable grasping from 0.25 m/s of the object detection-based baseline method to 0.40 m/s. Compared to Simple Curriculum Learning （SimpleCL） with only increasing difficulty， the proposed method increases the success rate by 3.6 percentage points and reduces the average joint acceleration and jerk norm by 58.34% and 69.25%， respectively， in the most difficult test. In physical experiments， the grasping success rates of the proposed method for static scene and two dynamic scenes are 95.0%， 90.0%， and 70.0%， respectively. It can be seen that this method effectively coordinates success rate and smoothness in robotic dynamic grasping tasks by jointly optimizing task difficulty and behavioral constraints.

Aiming at the problem that the universal perturbation search in HGAA （Hyperspherical General Adversarial Attacks） algorithm is always limited to the spatial spherical surface， and it does not have the ability to search the space inside the sphere， a differential evolution algorithm based on hypersphere was proposed. In the algorithm， the search space was expanded to the interior of the sphere， and Differential Evolution （DE） algorithm was used to search the optimal sphere， so as to generate universal perturbations with higher fooling rate and lower modulus length on this sphere. Besides， the influence of key parameters such as the number of populations on the algorithm was analyzed， and the performance of the universal perturbations generated by the algorithm on different neural network models was tested. The algorithm was verified on CIFAR10 and SVHN image classification datasets， and the fooling rate of the algorithm was increased by up to 11.8 percentage points compared with that of HGAA algorithm. Experimental results show that this algorithm extends the universal perturbation search space of the HGAA algorithm， reduces the modulus length of universal perturbation， and improves the fooling rate of universal perturbations.

To solve the problem that the existing IP covert timing channel need to make agreed the encoding scheme between the information and IP packet timing interval, can’t dynamic adjustment of double-blind according to the network transmission quality in the process of sending and receiving, a double-blind dynamic adjustment strategy what is used to negotiate IP covert timing channel encoding scheme is proposed. By segmenting the network environment and depending on the dynamic network environment, the strategy selected the default encoding scheme, achieved double-blind dynamic adjustment of encoding scheme between sender and receiver. In order to verify the reliability of strategy, the experimental environment of covert timing channel is constructed. The results show that the proposed method can achieve double-blind dynamic adjustment between sender and receiver.