Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the limitations of the existing outlier detection methods, we propose a novel outlier detection algorithm which is named as kNN-LOF. First, the k-nearest neighbors algorithm is applied to divide different areas for outlier attributes, which is more suitable for outlier detection in different density distributions. Secondly, a hierarchical adjacency order is proposed to hierarchize the neighborhood range according to the link distance. The average sequence distance is calculated from the data objects in the hierarchy, and the reachable distance of an object is redefined to introduce a new local outlier factor. Experimental results show that the proposed algorithm has good performance in improving the accuracy of outlier detection.

With the fast development of the Internet of Things, Radio Frequency Identification (RFID) has been widely applied into many areas. Nevertheless, security problems of the RFID technology are also gradually exposed, when it provides life convenience. In particular, the appearance of a large number of fake and counterfeit goods has caused massive loss for both producers and customers, for which the clone tag is a serious security threat. If attackers acquire the complete information of a tag, they can then obtain the unique identifier of the tag by some technological means. In general, because there is no extra identifier of a tag, it is difficult to distinguish an original tag and its clone one. Once the legal tag data is obtained, attackers can be able to clone this tag. Therefore, this paper shows an efficient RFID mutual verification protocol. This protocol is based on the Physical Unclonable Function (PUF) and the lightweight cryptography to achieve efficient verification of a single tag. The protocol includes three process: tag recognition, mutual verification and update. The tag recognition is that the reader recognizes the tag; mutual verification is that the reader and tag mutually verify the authenticity of each other; update is supposed to maintain the latest secret key for the following verification. Analysis results show that this protocol has a good balance between performance and security.

Radio frequency identification is one of the key techniques for Internet of Things, which has been widely adopted in many applications for identification. However, there exist various security and privacy issues in radio frequency identification (RFID) systems. Particularly, one of the most serious threats is to clone tags for the goal of counterfeiting goods, which causes great loss and danger to customers. To solve these issues, lots of authentication protocols are proposed based on physical unclonable functions that can ensure an anti-counterfeiting feature. However, most of the existing schemes require secret parameters to be stored in tags, which are vulnerable to physical attacks that can further lead to the breach of forward secrecy. Furthermore, as far as we know, none of the existing schemes are able to solve the security and privacy problems with good scalability. Since many existing schemes rely on exhaustive searches of the backend server to validate a tag and they are not scalable for applications with a large scale database. Hence, in this paper, we propose a lightweight RFID mutual authentication protocol with physically unclonable functions (PUFs). The performance analysis shows that our proposed scheme can ensure security and privacy efficiently in a scalable way.

The second generation of blockchain represented by smart contracts has been developing vigorously in recent years. However, frequent smart contract vulnerability incidents pose a serious risk to blockchain ecosystem security. Since current symbol execution tools often fall into path explosion and thus lead to inefficient detection, this paper expands Mythril's framework to optimize its performance. Firstly, it finds out potential vulnerable code regions using static analysis and identifies critical paths that may have security defects. Then, aiming at the problem that traditional search algorithms cannot actively locate and explore critical paths, this paper presents a multi-objective oriented path search (MOPS) strategy based on path priority. This strategy guides dynamic symbolic execution to cover critical paths quickly, avoiding blind traversal of program execution paths. Finally, it describes security rules and proposes corresponding detection logics for different vulnerability categories. This paper analyzes over 1000 smart contracts extracted from Etherscan. Compared with existing tools based on symbolic execution, the proposed method can reduce time consumption by around 35% while ensuring the accuracy of vulnerability detection. Moreover, existing tools often issue warnings that do not actually cause financial losses. But the proposed method only concentrates on code regions related to transfer of funds, so it can reduce the false alarm rate to some extent.

The Internet of Things (IoT) has been integrated into legacy healthcare systems for the purpose of improving healthcare processes. As one of the key technologies of IoT, radio frequency identification (RFID) technology has been applied to offer services like patient monitoring, drug administration, and medical asset tracking. However, people have concerns about the security and privacy of RFID-based healthcare systems, which require a proper solution. To solve the problem, recently in 2019, Fan et al. proposed a lightweight RFID authentication scheme in the IEEE Network. They claimed that their scheme can resist various attacks in RFID systems with low implementation cost, and thus is suitable for RFID-based healthcare systems. In this article, our contributions mainly consist of two parts. First, we analyze the security of Fan et al.’s scheme and find out its security vulnerabilities. Second, we propose a novel lightweight authentication scheme to overcome these security weaknesses. The security analysis shows that our scheme can satisfy the necessary security requirements. Besides, the performance evaluation demonstrates that our scheme is of low cost. Thus, our scheme is well-suited for practical RFID-based healthcare systems.