Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Yang, Jiahai"'
Publikováno v:
2022 IEEE 30th International Conference on Network Protocols (ICNP), pp. 1-11
Network utility maximization (NUM) is a well-studied problem for network traffic management and resource allocation. Because of the inherent decentralization and complexity of networks, most researches develop decentralized NUM algorithms. In recent
Externí odkaz:
http://arxiv.org/abs/2408.08034
Autor:
Dong, Cong, Yang, Jiahai, Li, Yun, Wu, Yue, Chen, Yufan, Li, Chenglong, Jiao, Haoran, Yin, Xia, Liu, Yuling
In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfusca
Externí odkaz:
http://arxiv.org/abs/2403.12363
Publikováno v:
Network and Distributed System Security Symposium (NDSS) 2023
Active Internet measurements face challenges when some measurements require many remote vantage points. In this paper, we propose a novel technique for measuring remote IPv6 networks via side channels in ICMP rate limiting, a required function for IP
Externí odkaz:
http://arxiv.org/abs/2210.13088
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Autor:
Wang, Su, Wang, Zhiliang, Zhou, Tao, Yin, Xia, Han, Dongqi, Zhang, Han, Sun, Hongbin, Shi, Xingang, Yang, Jiahai
Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent Threats (APT), are commonly adopted by modern attackers. Recent studies propose leveraging the rich contextual information in data provenance to detect threats i
Externí odkaz:
http://arxiv.org/abs/2111.04333
Autor:
Han, Dongqi, Wang, Zhiliang, Chen, Wenqi, Zhong, Ying, Wang, Su, Zhang, Han, Yang, Jiahai, Shi, Xingang, Yin, Xia
Unsupervised Deep Learning (DL) techniques have been widely used in various security-related anomaly detection applications, owing to the great promise of being able to detect unforeseen threats and superior performance provided by Deep Neural Networ
Externí odkaz:
http://arxiv.org/abs/2109.11495
Autor:
Chen, Wenqi, Wang, Zhiliang, Chang, Liyuan, Wang, Kai, Zhong, Ying, Han, Dongqi, Duan, Chenxin, Yin, Xia, Yang, Jiahai, Shi, Xingang
Publikováno v:
In Computer Networks June 2024 247
Publikováno v:
In Computers & Security March 2024 138
DGA-based botnet, which uses Domain Generation Algorithms (DGAs) to evade supervision, has become a part of the most destructive threats to network security. Over the past decades, a wealth of defense mechanisms focusing on domain features have emerg
Externí odkaz:
http://arxiv.org/abs/2009.09959
Autor:
Han, Dongqi, Wang, Zhiliang, Zhong, Ying, Chen, Wenqi, Yang, Jiahai, Lu, Shuqiang, Shi, Xingang, Yin, Xia
Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to be extremely vulnerable to adversarial attacks, especially in such se
Externí odkaz:
http://arxiv.org/abs/2005.07519
Publikováno v:
In Computer Networks December 2023 237