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Li, Zhixin, Liu, Lei, Kong, Degang.  2019.  Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model. 2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS). :700–703.

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

Li, Qingyuan, Wu, Hao, Liu, Lei, Pan, Bin, Dong, Lan.  2018.  A Group based Dynamic Mix Zone Scheme for Location Privacy Preservation in VANETs. 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC). :1–5.
Modern vehicles are equipped with wireless communication technologies, allowing them to communicate with each other. Through Dedicated Short Range Communication (DSRC), vehicles periodically broadcast beacons messages for safety applications, which gives rise to disclosure of location privacy. A way to protect vehicles location privacy is to have their pseudonyms changed frequently. With restrict to limited resources (such as computation and storage), we propose a group based dynamic mix zone scheme, in which vehicles form a group when their pseudonyms are close to expire. Simulation results confirm that the proposed scheme can protect location privacy and alleviate the storage burden.
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Gao, Hongcan, Zhu, Jingwen, Liu, Lei, Xu, Jing, Wu, Yanfeng, Liu, Ao.  2019.  Detecting SQL Injection Attacks Using Grammar Pattern Recognition and Access Behavior Mining. 2019 IEEE International Conference on Energy Internet (ICEI). :493–498.
SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks.