摘要：
Malicious behavior detection is a key topic that has been a focus in the field of intrusion detection. Current intrusion detection systems are primarily based on single-point monitoring and detection and cannot detect attack modes with a hidden attack frequency. The idea presented in this paper is the incorporation of API call sequence software into the analysis and the construction of behavior chains to express the behavior patterns in software. This paper introduces related definitions of behavioral points and behaviors and proposes a depth-detection method for malware based on behavior chains (MALDC). The method monitors behavior points based on API calls and then uses the calling sequence of those behavior points at runtime to construct a behavior chain. Finally, we use depth detection method based on long short-term memory(LSTM) to detect malicious behavior from the behavior chains. To verify the performance of the proposed model, we conducted a large experiment on 54,324 malware and 53,361 benign samples collected from Windows systems and used those samples to train and test the model. Comparative verification by using various classifiers showed that the behavior points extracted based on the above method and the constructed behavior chains can be used to recognize malicious behavior at a high recognition rate. The method achieved an accuracy of 98.64% with a false positive rate of less than 2% in the best case, which is a satisfactory recognition rate for detecting malicious software behavior.
期刊：
Journal of Cloud Computing,2020年9(1):1-17 ISSN：2192-113X
通讯作者：
Zhang, Hao
作者机构：
[Li, Jia; Liu, Sanya; Zhang, Hao; Huang, Tao; Xia, Yu; Yang, Huali] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Li, Jia; Liu, Sanya; Zhang, Hao; Huang, Tao; Xia, Yu; Yang, Huali] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Yin, Hao] Shenyang Univ, Coll Informat Engn, Shenyang, Peoples R China.
通讯机构：
[Zhang, Hao] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
期刊：
ASSE '20: Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference,2020年:Pages 100–104
作者机构：
[Zhang, Hao; Xia, Yu; Huang, Tao; Li, Jia; Wang, HaiJin] National Engineering Research Center for E-Learning, National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China
会议名称：
2020 Asia Service Sciences and Software Engineering Conference, ASSE 2020
会议时间：
May 13, 2020 - May 15, 2020
会议地点：
Nagoya, Japan
会议论文集名称：
ASSE '20: Proceedings of the 2020 Asia Service Sciences and Software Engineering Conference
摘要：
With the rise of blockchain technology, data sharing between organizations is often established in a distributed blockchain ledger that is decentralized and tamperproof and has a low trust cost. However, traditional blockchain technology does not adequately support the on-chain storage of massive data, and all the on-chain stored data are completely open and transparent to participants; consequently, it is impossible to meet the user's needs for privacy protection. In this study, we designed a scalable data access control method for blockchains; this method extends the storage forms of the blockchain to support the chaining and sharing of large files, ensures that the traceable data in the chaining process cannot be tampered with, and introduces the attribute authority mechanism in peer nodes to improve the reliability and efficiency of attribute authorization. To verify the correctness and security of the method, we built a complete prototype system based on the Hyperledger Fabric license chain and performed rigorous evaluations on the indicators of physical resource consumption and performance using the Hyperledger Caliper evaluation model. The results showed that the proposed method achieved a good balance in terms of performance, safety, and resource consumption indicators.
摘要：
With the rapid development of MOOC platforms, the online learning resources are increasing. Because learners differ in terms of cognitive ability and knowledge structure, they cannot rapidly identify learning resources in which they are interested. Traditional collaborative filtering recommendation technologies perform poorly given sparse data and cold starts. Furthermore, the redundant recommended content and the high-dimensional and nonlinear data on online learning users cannot be effectively handled, leading to inefficient resource recommendations. To enhance learner efficiency and enthusiasm, this paper presents a highly accurate resource recommendation model (MOOCRC) based on deep belief networks (DBNs) in MOOC environments. This method deeply mines learner features and course content attribute features and incorporates learner behavior features to build user-course feature vectors as inputs to the deep model. Learner ratings of courses are processed as supervised labels with supervised learning. The MOOCRC model is trained by unsupervised pretraining and supervised feedback fine tuning; moreover, the model is obtained by adjusting the model parameters repeatedly. To verify the effectiveness of the MOOCRC, an experimental analysis is conducted using learner elective data obtained from the starC MOOC platform of Central China Normal University. Real course enrollment data are used to verify the classification accuracy of the MOOCRC. The experimental results show that the MOOCRC has greater recommendation accuracy and converges more quickly than traditional recommendation methods.
摘要：
With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user's courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.
期刊：
ICDTE '17: Proceedings of the 1st International Conference on Digital Technology in Education,2017年Part F131203:Pages 79–83
作者机构：
[Chen, Yun; Zhang, Hao; Huang, Tao] National Engineering Research Center for E-Learning, Central China Normal University(CCNU), Wuhan, 430079, China
会议名称：
978-1-4503-5283-3
会议时间：
August, 2017
会议地点：
Taipei Taiwan
会议论文集名称：
ICDTE '17: Proceedings of the International Conference on Digital Technology in Education
摘要：
The construction of learning object repository is the key infrastructure of learning object organization under the E-learning environment. At present, the learning object repository is redundant construction, scattered and confused, lack the creation of tacit knowledge into explicit knowledge, is not conducive to knowledge sharing. In view of the above problems, this paper proposed a learning object repository fusion method, designed and constructed learning object knowledge units and fusion rule base, and applied fuzzy set theory to knowledge unit similarity fusion. Finally, we verified the effectiveness and feasibility of the method through the experiments, and get the more reliable results than single knowledge source detection, reduced the uncertainty of the fusion results. It has a certain reference value for improving the quality of learning object repository and collaborative work among repositories.
摘要：
Based on the cognitive load theory and the interactive resources, this paper aims to design a model of interactive learning resources to reduce students' cognitive load. In particular, the interactive learning resources were designed based on HTML5 technology, which performed a teaching evaluation around a geography lecture in junior high school. It has been the results proved that it is very effective for students to reduce their cognitive load in terms of the feedback received from both teachers and students.
摘要：
With the rapid development of MOOC platform, MOOC resource recommendation technology is emerging to improve the learner's learning efficiency. The traditional collaborative filtering resource recommendation technique is ineffective when dealing with sparse data and cannot accurately handle the high dimension attributes of online learning users, which result in low efficiency of resource recommendation. In order to solve this problem, this paper proposes a personalized recommendation system based on DBN in MOOC environment, which utilizes the high performance of DBN in function approximation, feature extraction, prediction classification and other aspects. It combines MOOC platform user - course feature vector to mine the user's course interests. Meanwhile, it uses the score of courses as the class label of DBN supervised learning. Through unsupervised pre-training and supervised feedback fine-tuning, the DBN recommendation model training can be achieved. The experiment was carried out in the real MOOC platform "starc", and was compared with several traditional recommendation methods. The experimental results show that the DBNCF is more efficient than the traditional cooperative filtering method.
