Deep learning, as an important branch of machine learning, has been widely applied in computer vision, natural language processing, speech recognition, and more. However, recent studies have revealed that deep learning systems are vulnerable to backdoor attacks. Backdoor attackers inject a hidden backdoor into the deep learning model, such that the predictions of the infected model will be maliciously changed if the hidden backdoor is activated by input with a backdoor trigger while behaving normally on any benign sample. This kind of attack can potentially result in severe consequences in the real world. Therefore, research on defending against backdoor attacks has emerged rapidly. In this article, we have provided a comprehensive survey of backdoor attacks, detections, and defenses previously demonstrated on deep learning. We have investigated widely used model architectures, benchmark datasets, and metrics in backdoor research and have classified attacks, detections and defenses based on different criteria. Furthermore, we have analyzed some limitations in existing methods and, based on this, pointed out several promising future research directions. Through this survey, beginners can gain a preliminary understanding of backdoor attacks and defenses. Furthermore, we anticipate that this work will provide new perspectives and inspire extra research into the backdoor attack and defense methods in deep learning.

Shadowsocks(R) is a proxy software based on Socks5, which is the collective name of shadowsocks and shadowsocksR. Shadowsocks(R) is a private protocol without a handshake negotiation mechanism. Peng broke the confidentiality of shadowsocks by exploiting vulnerability in the shadowsocks protocol and decrypted the shadowsocks packets encrypted with none-AEAD encryption options using a redirection attack. Chen et al. started with the cryptographic algorithm used by shadowsocks(R) and preliminarily discussed the confidentiality of user data under the protection of shadowsocks(R) in theory. Based on Chen’s work, this paper further clarifies the shadowsocks(R) protocol format and studies the encryption mechanism of shadowsocks(R) from the perspective of protocol analysis. The vulnerability of the shadowsocks(R) encryption mechanism is found, and an attack method of shadowsocks(R) is proposed. The attack method is a passive attack and can decrypt the shadowsocks packets encrypted with any encryption option. Compared with Peng’s attack method, the method is more effective and more suitable for actual attacks. Finally, some methods to improve the protocol security of shadowsocks(R) are proposed.

Pneumatic actuators are low-cost, safe, clean, and exhibit a high power to weight ratio. In this paper a novel servo pneumatic system based on a miniature cylinder with a 9.5 mm bore size is presented. Four low-cost 2-way proportional valves are incorporated to provide greater design flexibility than the traditional single 4-way servo valve solution. A nonlinear system model is developed and validated using open-loop experiments. The use of bipolynomial functions to model the valve flow rates provides a more accurate solution than the commonly used nozzle flow equations. A novel multiple-input single-output nonlinear position control law is designed using the backstepping method. The stability analysis includes the effects of friction modeling error and valve modeling error. In experiments with a 1.5 kg moving mass, the new control law produced maximum tracking errors of plusmn0.5 mm for a 1 Hz sine wave trajectory, and steady-state errors within plusmn0.05 mm. The tracking errors are 82% less than those produced by a linear controller