Biblio

The Zero Trust Architecture is an important part of the industrial Internet security protection standard. When analyzing industrial data for enterprise-level or industry-level applications, differential privacy (DP) is an important technology for protecting user privacy. However, the centralized and local DP used widely nowadays are only applicable to the networks with fixed trust relationship and cannot cope with the dynamic security boundaries in Zero Trust Architecture. In this paper, we design a differential privacy scheme that can be applied to Zero Trust Architecture. It has a consistent privacy representation and the same noise mechanism in centralized and local DP scenarios, and can balance the strength of privacy protection and the flexibility of privacy mechanisms. We verify the algorithm in the experiment, that using maximum expectation estimation method it is able to obtain equal or even better result of the utility with the same level of security as traditional methods.

With the development of information technology, the Internet of things (IOT) has gradually become the third wave of global information industry revolution after computer and Internet. Artificial intelligence (AI) and IOT technology is an important prerequisite for the rapid development of the current information society. However, while AI and IOT technologies bring convenient and intelligent services to people, they also have many defects and imperfect development. Therefore, it is necessary to pay more attention to the development of AI and IOT technologies, actively improve the application system, and create a network security management system for AI and IOT applications that can timely detect intrusion, assess risk and prevent viruses. In this paper, the network security risks caused by AI and IOT applications are analyzed. Therefore, in order to ensure the security of IOT environment, network security and privacy security have become the primary problems to be solved, and management should be strengthened from technical to legal aspects.

With the development of network, network security has become a topic of increasing concern. Recent years, machine learning technology has become an effective means of network intrusion detection. However, machine learning technology requires a large amount of data for training, and training data often contains privacy information, which brings a great risk of privacy leakage. At present, there are few researches on data privacy protection in the field of intrusion detection. Regarding the issue of privacy and security, we combine differential privacy and machine learning algorithms, including One-class Support Vector Machine (OCSVM) and Local Outlier Factor(LOF), to propose an hybrid intrusion detection system (IDS) with privacy protection. We add Laplacian noise to the original network intrusion detection data set to get differential privacy data sets with different privacy budgets, and proposed a hybrid IDS model based on machine learning to verify their utility. Experiments show that while protecting data privacy, the hybrid IDS can achieve detection accuracy comparable to traditional machine learning algorithms.

Intelligent recommendation applications based on data mining have appeared as prospective solution for consumer's demand recognition in large-scale data, and it has contained a great deal of consumer data, which become the most valuable wealth of application providers. However, the increasing threat to consumer privacy security in intelligent recommendation mobile application (IR App) makes it necessary to have a risk evaluation to narrow the gap between consumers' need for convenience with efficiency and need for privacy security. For the previous risk evaluation researches mainly focus on the network security or information security for a single work, few of which consider the whole data lifecycle oriented privacy security risk evaluation, especially for IR App. In this paper, we analyze the IR App's features based on the survey on both algorithm research and market prospect, then provide a hierarchical factor set based privacy security risk evaluation method, which includes whole data lifecycle factors in different layers.

Nowadays, encrypted traffic classification has become a challenge for network monitoring and cyberspace security. However, the existing methods cannot meet the requirements of encrypted traffic classification because of the encryption protocol in communication. Therefore, we design a novel neural network named Stereo Transform Neural Network (STNN) to classify encrypted network traffic. In STNN, we combine Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) based on statistical features. STNN gains average precision about 95%, average recall about 95%, average F1-measure about 95% and average accuracy about 99.5% in multi-classification. Besides, the experiment shows that STNN obviously accelerates the convergence rate and improves the classification accuracy.

In the RFID technology, the privacy of low-cost tag is a hot issue in recent years. A new mutual authentication protocol is achieved with the time stamps, hash function and PRNG. This paper analyzes some common attack against RFID and the relevant solutions. We also make the security performance comparison with original security authentication protocol. This protocol can not only speed up the proof procedure but also save cost and it can prevent the RFID system from being attacked by replay, clone and DOS, etc..