Visible to the public Biblio

Filters: Author is Cheng, Liang  [Clear All Filters]
2020-03-16
Yang, Huan, Cheng, Liang, Chuah, Mooi Choo.  2019.  Deep-Learning-Based Network Intrusion Detection for SCADA Systems. 2019 IEEE Conference on Communications and Network Security (CNS). :1–7.
Supervisory Control and Data Acquisition (SCADA)networks are widely deployed in modern industrial control systems (ICSs)such as energy-delivery systems. As an increasing number of field devices and computing nodes get interconnected, network-based cyber attacks have become major cyber threats to ICS network infrastructure. Field devices and computing nodes in ICSs are subjected to both conventional network attacks and specialized attacks purposely crafted for SCADA network protocols. In this paper, we propose a deep-learning-based network intrusion detection system for SCADA networks to protect ICSs from both conventional and SCADA specific network-based attacks. Instead of relying on hand-crafted features for individual network packets or flows, our proposed approach employs a convolutional neural network (CNN)to characterize salient temporal patterns of SCADA traffic and identify time windows where network attacks are present. In addition, we design a re-training scheme to handle previously unseen network attack instances, enabling SCADA system operators to extend our neural network models with site-specific network attack traces. Our results using realistic SCADA traffic data sets show that the proposed deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerged threats.
2020-01-27
Li, Zhangtan, Cheng, Liang, Zhang, Yang.  2019.  Tracking Sensitive Information and Operations in Integrated Clinical Environment. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :192–199.
Integrated Clinical Environment (ICE) is a standardized framework for achieving device interoperability in medical cyber-physical systems. The ICE utilizes high-level supervisory apps and a low-level communication middleware to coordinate medical devices. The need to design complex ICE systems that are both safe and effective has presented numerous challenges, including interoperability, context-aware intelligence, security and privacy. In this paper, we present a data flow analysis framework for the ICE systems. The framework performs the combination of static and dynamic analysis for the sensitive data and operations in the ICE systems. Our experiments demonstrate that the data flow analysis framework can record how the medical devices transmit sensitive data and perform misuse detection by tracing the runtime context of the sensitive operations.