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2021-08-31
Sundar, Agnideven Palanisamy, Li, Feng, Zou, Xukai, Hu, Qin, Gao, Tianchong.  2020.  Multi-Armed-Bandit-based Shilling Attack on Collaborative Filtering Recommender Systems. 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). :347–355.
Collaborative Filtering (CF) is a popular recommendation system that makes recommendations based on similar users' preferences. Though it is widely used, CF is prone to Shilling/Profile Injection attacks, where fake profiles are injected into the CF system to alter its outcome. Most of the existing shilling attacks do not work on online systems and cannot be efficiently implemented in real-world applications. In this paper, we introduce an efficient Multi-Armed-Bandit-based reinforcement learning method to practically execute online shilling attacks. Our method works by reducing the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach. Such practical online attacks open new avenues for research in building more robust recommender systems. We treat the recommender system as a black box, making our method effective irrespective of the type of CF used. Finally, we also experimentally test our approach against popular state-of-the-art shilling attacks.
2021-05-13
Shu, Fei, Chen, Shuting, Li, Feng, Zhang, JianYe, Chen, Jia.  2020.  Research and implementation of network attack and defense countermeasure technology based on artificial intelligence technology. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). :475—478.
Using artificial intelligence technology to help network security has become a major trend. At present, major countries in the world have successively invested R & D force in the attack and defense of automatic network based on artificial intelligence. The U.S. Navy, the U.S. air force, and the DOD strategic capabilities office have invested heavily in the development of artificial intelligence network defense systems. DARPA launched the network security challenge (CGC) to promote the development of automatic attack system based on artificial intelligence. In the 2016 Defcon final, mayhem (the champion of CGC in 2014), an automatic attack team, participated in the competition with 14 human teams and once defeated two human teams, indicating that the automatic attack method generated by artificial intelligence system can scan system defects and find loopholes faster and more effectively than human beings. Japan's defense ministry also announced recently that in order to strengthen the ability to respond to network attacks, it will introduce artificial intelligence technology into the information communication network defense system of Japan's self defense force. It can be predicted that the deepening application of artificial intelligence in the field of network attack and defense may bring about revolutionary changes and increase the imbalance of the strategic strength of cyberspace in various countries. Therefore, it is necessary to systematically investigate the current situation of network attack and defense based on artificial intelligence at home and abroad, comprehensively analyze the development trend of relevant technologies at home and abroad, deeply analyze the development outline and specification of artificial intelligence attack and defense around the world, and refine the application status and future prospects of artificial intelligence attack and defense, so as to promote the development of artificial intelligence attack and Defense Technology in China and protect the core interests of cyberspace, of great significance
2020-04-06
Chen, Yuxiang, Dong, Guishan, Bai, Jian, Hao, Yao, Li, Feng, Peng, Haiyang.  2019.  Trust Enhancement Scheme for Cross Domain Authentication of PKI System. 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :103–110.
Public Key Infrastructure (PKI) has been popularized in many scenarios such as e-government applications, enterprises, etc. Due to the construction of PKI system of various regions and departments, there formed a lot of isolated PKI management domains, cross-domain authentication has become a problem that cannot ignored, which also has some traditional solutions such as cross-authentication, trust list, etc. However, some issues still exist, which hinder the popularity of unified trust services. For example, lack of unified cross domain standard, the update period of Certificate Revocation List (CRL) is too long, which affects the security of cross-domain authentication. In this paper, we proposed a trust transferring model by using blockchain consensus instead of traditional trusted third party for e-government applications. We exploit how to solve the unified trust service problem of PKI at the national level through consensus and transfer some CA management functions to the blockchain. And we prove the scheme's feasibility from engineering perspective. Besides, the scheme has enough scalability to satisfy trust transfer requirements of multiple PKI systems. Meanwhile, the security and efficiency are also guaranteed compared with traditional solutions.
2020-02-17
Guo, Qingrui, Xie, Peng, Li, Feng, Guo, Xuerang, Li, Yutao, Ma, Lin.  2019.  Research on Linkage Model of Network Resource Survey and Vulnerability Detection in Power Information System. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1068–1071.
this paper first analyses the new challenges of power information network management, difficulties of the power information network resource survey and vulnerability detection are proposed. Then, a linkage model of network resource survey and vulnerability detection is designed, and the framework of three modules in the model is described, meanwhile the process of network resources survey and vulnerability detection linkage is proposed. Finally, the implementation technologies are given corresponding to the main functions of each module.