As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Internet of Things: A survey on machine learning-based intrusion detection approaches

The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.

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Anatomy of Threats to the Internet of Things

A survey on internet of vehicles: Applications, security issues & solutions

The goal of intelligent transport systems (ITSs) is to enhance the network performance of vehicular ad hoc networks (VANETs). Even though it presents new opportunities to the Internet of Vehicles (IoV) environment, there are some security concerns including the need to establish trust among the connected peers. The fifth-generation (5G) communication system, which provides reliable and low-latency communication services, is seen as the technology to cater for the challenges in VANETs. The incorporation of software-defined networks (SDNs) also ensures an effective network management. However, there should be monitoring and reporting services provided in the IoV. Blockchain, which has decentralization, transparency, and immutability as some of its properties, is designed to ensure trust in networking platforms. In that regard, this article analyzes the combination of blockchain and SDN for the effective operation of the VANET systems in 5G and fog computing paradigms. With managerial responsibilities shared between the blockchain and the SDN, it helps to relieve the pressure off the controller due to the ubiquitous processing that occurs. A trust-based model that curbs malicious activities in the network is also presented. The simulation results substantially guarantee an efficient network performance, while also ensuring that there is trust among the entities.

A Blockchain-SDN-Enabled Internet of Vehicles Environment for Fog Computing and 5G Networks

As a typical application of Internet of Things (IoT) in the field of transportation, Internet of Vehicles (IoV) aims at achieving an integrated intelligent transportation system to enhance traffics efficiency, avoid accidents, ensure road safety, and improve driving experiences by using new IoT technologies. Different from other Internet, it is characterized by dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation. Due to these characteristics, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks, etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Attacks and countermeasures in the internet of vehicles

Internet of Vehicles (IoV) is a typical application of Internet of things in the field of transportation, which aims at achieving an integrated intelligent transportation system to enhance traffics, to avoid accidents, to ensure road safety, and to improve driving experiences. Due to its characteristics of dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks and etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Security and Privacy in the Internet of Vehicles

We are developing an opportunistic communication based wireless sensor network system to gather and analyze the data of sediment transport in the desert for desertification research. There are two kinds of nodes in our system, one is base station and the other is sensor node, which can move randomly driven by wind and forward the data to the base station while they are encountering. Science the number of nodes has large impact on performance like success rate of data forward, lifetime and cost of the system, it is significant to estimate the lower bound for the required number of sensor nodes. In this paper, through some assumption and simplification, we give a simple formula to estimate the minimum number of sensor nodes that the system need to ensure opportunistic encounter between nodes will occur to make the data can be forwarded.

Lower Bound Estimation for Required Number of Nodes in the Opportunistic Communication – Based Wireless Sensor Network

This paper designs a malware detection system under the high-speed network environment. In the system, all the traffic of the targeted network is captured by zero copy technology, then they are parsed and filtered by the protocol analysis engine, which restoring the original contents on the network. Finally, we take advantage of the Dynamic Binary Platform DynamoRIO with the characteristics of the buffer overflow attack analysis to detect the malware program of the network. Experimental results show that this system can effectively detect the malware program and outperform the popular antivirus software in the high-speed network environment.

DynamoRIO-Based Malware Detection System under 10G Network Environment

Intermittent Connected Mobile Sensor Networks (ICMSN) have emerged in recent studies, e.g. underwater sensor networks, wildlife tracking and human-oriented flu virus monitoring. In these networks, conventional routing protocols in WSN are not suitable for data gathering application in ICMSN since packets will be dropped when no complete route to the sink is available. In this paper, we propose an efficient data gathering scheme for ICMSN, which is composed of forwarding protocol and buffer management. Message forwarding makes decision on which sensors are qualified for next hop based on nodal delivery probability, which signifies the likelihood that the sensor can deliver the message to the sink successfully based on the nodal mobility pattern and residual energy. Buffer management consists of two related components: queuing mechanism and purging mechanism. The former determines priority of messages to transmit based on the importance factor, which indicates importance of the messages and QoS requirement of transmission. The latter utilizes a death vector generated by the sink to purge useless messages that have been delivered from the network. Our experimental results show that the proposed data gathering scheme supports the different transmission requirement of messages and achieves the good performance tradeoff between delivery ratio, delay and resource consumption.

Tao Zhang

Papers

Attacks and countermeasures in the internet of vehicles

Security and Privacy in the Internet of Vehicles

Lower Bound Estimation for Required Number of Nodes in the Opportunistic Communication – Based Wireless Sensor Network

DynamoRIO-Based Malware Detection System under 10G Network Environment

Efficient Data Gathering in Intermittent Connected Mobile Sensor Networks