Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we propose a novel word embedding mechanism to extract packet semantic meanings and adopt LSTM to learn the temporal relation among fields in the packet header and for further classifying whether an incoming packet is normal or a part of malicious traffic. The evaluation results on ISCX2012, USTC-TFC2016, IoT dataset from Robert Gordon University and IoT dataset collected on our Mirai Botnet show that our approach is competitive to the prior literature which detects malicious traffic at the flow level. While the network traffic is booming year by year, our first attempt can inspire the research community to exploit the advantages of deep learning to build effective IDSs without suffering significant detection delay.

Many nations are promoting the green transition in the energy sector to attain neutral carbon emissions by 2050. SG2 is expected to explore data-driven analytics and enhance communication technologies to improve the efficiency and sustainability of distributed renewable energy systems. These features are beyond smart metering and electric surplus distribution in conventional smart grids. Given the high dependence on communication networks to connect distributed microgrids in SG2, potential cascading failures of connectivity can cause disruption to data synchronization to the remote control systems. This paper reviews security threats and defense tactics for three stakeholders: power grid operators, communication network providers, and consumers. Through the survey, we found that SG2’s stakeholders are particularly vulnerable to substation attacks/vandalism, malware/ransomware threats, blockchain vulnerabilities and supply chain breakdowns. Furthermore, incorporating artificial intelligence (AI) into autonomous energy management in distributed energy resources of SG2 creates new challenges. Accordingly, adversarial samples and false data injection on electricity reading and measurement sensors at power plants can fool AI-powered control functions and cause messy error-checking operations in energy storage, wrong energy estimation in electric vehicle charging, and even fraudulent transactions in peer-to-peer energy trading models. Scalable blockchain-based models, physical unclonable function, interoperable security protocols, and trustworthy AI models designed for managing distributed microgrids in SG2 are typical promising protection models for future research.

Low Power Wireless (LPW) networks have recently been emerging as key candidates to offer affordable connectivity for the constrained devices in the Internet of Things (IoT). However, they also raise major security concerns due to the inherent security vulnerabilities of built-in communication protocols. By exploiting these flaws, an adversary can attack sensors or actuators in an LPW network and force them to execute energy-hungry tasks such as verifying unauthenticated garbage messages repeatedly. This attack, namely energy depletion attack (EDA), can drain the batteries of the devices rapidly and lead to soaring network-wide energy expenditure. Consequently, the offense can leave the victims disabled, and even shut down the whole network due to the battery exhaustion of all the devices. In this paper, we investigate existing studies and provide a systematic review of EDAs and defenses in LPW networks. Through this work, we conclude that most existing LPW technologies are vulnerable to EDAs. This paper also indicates the security challenges in LPW networks related to EDAs along with the potential research directions. While LPW technologies have already hit the market with the promising deployment schedules, our attempt can inspire the research community to enhance the security of underlying protocols that will shape the connectivity of billions of devices in the future IoT ecosystem.

Next-generation advanced driver-assistance systems (ADAS) and cooperative adaptive cruise control (CACC) for advanced/autonomous driving are expected to increasingly use wireless connectivity such as V2V and V2I to improve the coverage, particularly in the locations where a vehicle's camera or radar is ineffective. However, using shared sensing data raises grave concerns about the truthfulness of information reported by unreliable stakeholders. For example, a transmitting vehicle may deliberately disseminate false locations to the surrounding receivers. Trusting the data, the automatic control systems in such connected receivers can be trapped to change to a wrong lane or accelerate unexpectedly, and then potentially lead to a crash. This work introduces a novel approach to support a host vehicle in verifying the motion behavior of a target vehicle and then the truthfulness of sharing data in cooperative vehicular communications. Initially, at the host vehicle, the detection system recreates the motion behavior of the target vehicle by extracting the positioning information from the V2V received messages. Furthermore, the next states of that vehicle are predicted based on the unscented Kalman filter. Unlike prior studies, the checkpoints of the predicted trajectory in the update stage are periodically corrected with a new reliable measurement source, namely 5G V2V multi-array beamforming localization. If there is any inconsistency between the estimated position and the corresponding reported one from V2V, the target vehicle will be classified as an abnormal one. The simulation results demonstrate that our method can achieve accuracy over 0.97 in detecting abnormal reports, including those from collusion and Sybil attacks.