Xixiang Lyu

Xidian University · School of Cyber Security

The increasing awareness of privacy preservation has led to a strong focus on mix networks (mixnets) protecting anonymity. As an efficient mixnet, cMix greatly reduces the latency, but brings privacy leakage risks due to the use of presetting mix nodes controlled by service providers. Besides, cMix is susceptible to blocking attacks that paralyze t...

Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing defense methods have demonstrated promising results, it is still not clear how to effectively remove backdoor-associated neurons in backdoored DNNs. In this paper, we pro...

With the rapid development of deep learning, its vulnerability has gradually emerged in recent years. This work focuses on backdoor attacks on speech recognition systems. We adopt sounds that are ordinary in nature or in our daily life as triggers for natural backdoor attacks. We conduct experiments on two datasets and three models to validate the...

With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage r...

Backdoor attack has emerged as a major security threat to deep neural networks (DNNs). While existing defense methods have demonstrated promising results on detecting and erasing backdoor triggers, it is still not clear if measures can be taken to avoid the triggers from being learned into the model in the first place. In this paper, we introduce t...

Anonymous access authentication schemes provide users with massive application services while protecting the privacy of users' identities. The identity protection schemes in 3G and 4G are not suitable for 5G anonymous access authentication due to complex computation and pseudonym asynchrony. In this paper, we consider mobile devices with limited re...

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing...

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the mod...

The distributed denial of service (DDoS) attack is detrimental to the industrial Internet of things (IIoT) as it triggers severe resource starvation on networked objects. Recent dynamics demonstrate that it is a highly profitable business for attackers using botnets. Current centralized mitigation solutions concentrate on detection and mitigation a...

According to the technical requirements for the new generation of integrated equipment in intelligent substations, a high voltage switch integrated monitoring device based on PowerPC processor was designed. This device integrates the functions of the individual mechanism monitoring IED, measuring IED, surge arrester monitoring IED and main monitori...

To eliminate the jitter at the very on and off points of high voltage breakers caused by mechanical vibration and EMI in their stroke displacement monitoring, a stroke displacement data fitting and processing algorithm based on least square method is presented. The derivative criterion of the original data of online monitoring is adopted to elimina...