Haidong Yang

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The current RFID systems are fragile to external attacks, due to the limitations of encryption authentication and physical protection methods used in implementation of RFID security systems. In this paper, we propose a collaborative RFID intrusion detection method that is based on an artificial immune system (AIS). The new method can enhance the security of(More)
Previous Research in network intrusion detection system (NIDS) has typically used misuse detection or supervised anomaly detection techniques. These techniques have difficulty in detecting new types of attacks or causing high false positives in real network environment. Unsupervised anomaly detection can overcome the drawbacks of misuse detection and(More)
for production service systems T. Qu, X.D. Chen, Yingfeng Zhang, Haidong Yang and George Q. Huang* Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Hong Kong, People’s Republic of China; Faculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, People’s Republic of China; Key Laboratory(More)
Radio Frequency Identification (RFID) technologies provide automatic and accurate object data capturing capability and enable real-time object visibility and traceability. Potential benefits have been widely reported for improving manufacturing shop-floor management. However, reports on how such potentials come true in real-life shopfloor daily operations(More)
Supervised anomaly intrusion detection systems (IDSs) based on Support Vector Machines (SVMs) classification technique have attracted much more attention today. In these systems, features of instances and the characteristic of kernels have great influence on learning and predict results. However, selecting feasible features and kernel parameters can be(More)
Existing production systems are short of real-time performance status of production process active perception, resulting in the production abnormal conditions processed lag, leading to the frequency problems of deviations in production tasks execution and planning. To address this problem, in this research, an advanced identification technology is extended(More)
Supervised anomaly intrusion detection systems (IDSs) based on Support Vector Machines (SVMs) classification technique have attracted much more attention today. In these systems, the characteristics of kernels have great in- fluence on learning and prediction results for IDSs. How- ever, selecting feasible parameters can be time-consuming as the number of(More)