Smart cities are expected to improve the quality of daily life, promote sustainable development, and improve the functionality of urban systems Now that many smart systems have been implemented, security and privacy issues have become a major challenge that requires effective countermeasures However, traditional cybersecurity protection strategies cannot be applied directly to these intelligent applications because of the heterogeneity, scalability, and dynamic characteristics of smart cities Furthermore, it is necessary to be aware of security and privacy threats when designing and implementing new mechanisms or systems Motivated by these factors, we survey the current situations of smart cities with respect to security and privacy to provide an overview of both the academic and industrial fields and to pave the way for further exploration Specifically, this survey begins with an overview of smart cities to provide an integrated context for readers Then, we discuss the privacy and security issues in current smart applications along with the corresponding requirements for building a stable and secure smart city In the next step, we summarize the existing protection technologies Finally, we present open research challenges and identify some future research directions

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Security and Privacy in Smart Cities: Challenges and Opportunities

The tremendous growth of interconnected things/devices in the whole world advances to the new paradigm, i.e., Internet of Things (IoT). The IoT use sensor-based embedded systems to interact with others, providing a wide range of applications and services to upper-level users. Undoubtedly, the data collected by the underlying IoTs are the basis of the upper-layer decision and the foundation for all the applications, which requires efficient energy protocols. Moreover, if the collected data are erroneous and untrustworthy, the data protection and application becomes an unrealistic goal, which further leads to unnecessary energy cost. However, the traditional methods cannot solve this problem efficiently and trustworthily. To achieve this goal, in this paper we design a novel energy-efficient and trustworthy protocol based on mobile fog computing. By establishing a trust model on fog elements to evaluate the sensor nodes, the mobile data collection path with the largest utility value is generated, which can avoid visiting unnecessary sensors and collecting untrustworthy data. Theoretical analysis and experimental results validate that our proposed architecture and method outperform traditional data collection methods in both energy and delay.

Energy-Efficient and Trustworthy Data Collection Protocol Based on Mobile Fog Computing in Internet of Things

Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.

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SCTSC: A Semicentralized Traffic Signal Control Mode With Attribute-Based Blockchain in IoVs

Nowadays, network technologies are essential for transferring and storing various information of users, companies, and industries. However, the growth of the information transfer rate expands the attack surface, offering a rich environment to intruders. Intrusion detection systems (IDSs) are widespread systems able to passively or actively control intrusive activities in a defined host and network perimeter. Recently, different IDSs have been proposed by integrating various detection techniques, generic or adapted to a specific domain and to the nature of attacks operating on. The cybersecurity landscape deals with tremendous diverse event streams that exponentially increase the attack vectors. Event stream processing (ESP) methods appear to be solutions that leverage event streams to provide actionable insights and faster detection. In this paper, we briefly describe domains (as well as their vulnerabilities) on which recent papers were-based. We also survey standards for vulnerability assessment and attack classification. Afterwards, we carry out a classification of IDSs, evaluation metrics, and datasets. Next, we provide the technical details and an evaluation of the most recent work on IDS techniques and ESP approaches covering different dimensions (axes): domains, architectures, and local communication technologies. Finally, we discuss challenges and strategies to improve IDS in terms of accuracy, performance, and robustness.

Intrusion Detection Systems: A Cross-Domain Overview

Internet of Things (IoT) brings the third development wave of the global information industry, which makes users, network, and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response, and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using a situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability, and network flow. Furthermore, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods.

Network Security Situation Awareness Based on Semantic Ontology and User-Defined Rules for Internet of Things

To solve the problems of noise interference and multistream tracing in anonymous network tracing, this article proposes an interval packet-size-based spread spectrum (IPS3) network flow watermarking technology, which adopts a new watermarking carrier based on the original direct sequence spread spectrum (DSSS) technology. On one hand, IPS3 solves the problem of multistream tracing through the operation of DSSS for original watermarking. On the other hand, taking the average packet size in a time interval as the watermarking carrier, it solves the problem of network flow watermarking technology being subjected to the network stability by adjusting the carrier size in the process of watermarking modulation. Experimental results show that the proposed IPS3 scheme outperforms the traditional network flow watermarking technology in the above-mentioned two issues: noise filtering and multistream tracing, which can achieve higher accuracy when it is used in anonymous network tracing. This article is part of a special issue on cybersecurity.

Flow Watermarking for Antinoise and Multistream Tracing in Anonymous Networks

TRM: Computing Reputation Score by Mining Reviews.

A web service security analysis model based on program slicing is proposed, which can be used to find existence of critical information disclosure vulnerabilities and proliferation of such vulnerabilities in a web service net, and eventually improve protection of critical information. Web service protocol is analyzed to obtain external service interfaces; source code is sliced to obtain interface information flow; critical information is checked to see whether it is disclosed through the interface information flow. Vulnerability proliferation of a service net is found through analyzing process of interface calling between two web services in which the critical information is transmitted and disclosed. A security report describing test results of a test scene is provided to verify the of security analysis process.

Yan Cao

Papers

Network Security Situation Awareness Based on Semantic Ontology and User-Defined Rules for Internet of Things

Flow Watermarking for Antinoise and Multistream Tracing in Anonymous Networks

TRM: Computing Reputation Score by Mining Reviews.

Web Service Security Analysis Model Based on Program Slicing