The spectrum efficiency (SE) and security of the secondary users (SUs) in the cognitive radio networks (CRNs) have become two main issues due to the limitation interference to the primary users (PUs) and the shared spectrum with the PUs. Intelligent reflecting surface (IRS) has been recently proposed as a revolutionary technique which can help to enhance the SE and physical layer security of wireless communications. This paper investigates the application of IRS in an underlay CRN, where a multi-antenna cognitive base station (CBS) utilizes spectrum assigned to the PU to communicate with a SU via IRS in the presence of multiple coordinated eavesdroppers (Eves). To achieve the trade-off between the secrecy rate (SR) and energy consumption, we investigate the secrecy energy efficiency (SEE) maximization problem by jointly designing the transmit beamforming at the CBS and the reflect beamforming at the IRS. To solve the non-convex problem with coupled variables, we propose an iterative alternating optimization algorithm to solve the sub-problems alternately, by utilizing an iterative penalty function based algorithm for sub-problem 1 and the difference of two-convex functions method for sub-problem 2. Furthermore, we provide a second-order-cone-programming (SOCP) approximation approach to reduce the computational complexity. Finally, the simulation results demonstrate that IRS can help significantly improve the SE and enhance the physical layer security in the CRNs. Moreover, the effectiveness and superiority of our proposed algorithm in achieving the trade-off between the SR and energy consumption are verified.

In vehicular ad hoc networks (VANETs), vehicles broadcast emergency messages and beacon messages, which enable drivers to perceive traffic conditions beyond their visual range thus improve driving safety. However, internal attackers can launch a false message attack for selfish purposes by reporting a non-existent traffic incident in emergency messages. Moreover, some collusion attackers may spread bogus beacon messages cooperatively to make the bogus traffic incident more deceptive. To improve the accuracy of false emergency message detection, we propose a novel intrusion detection system (IDS) based on time series classification and deep learning. Considering that traffic parameters are highly correlated with time, we collect time series of traffic parameters closely related to traffic incidents from messages of vehicles near reported traffic incidents as time series feature vectors. To recognize the pattern of traffic parameters changing over time more accurately, a traffic incident classifier based on long short-term memory (LSTM) is designed and trained using time series feature vectors from both normal and collusion attack scenarios. Based on the classification result, the authenticity of the emergency message can be determined. Finally, we evaluate the performance of the proposed LSTM-based IDS through extensive simulation. Simulation results validate that our IDS is more accurate in false message detection compared with some well-known machine learning-based schemes.

Cognitive radio networks (CRNs) and millimeter wave (mmWave) communications are two major technologies to enhance the spectrum efficiency (SE). Considering that the SE improvement in the CRNs is limited due to the interference temperature imposed on the primary user (PU), and the severe path loss and high directivity in mmWave communications make it vulnerable to blockage events, we introduce an intelligent reflecting surface (IRS) into mmWave CRNs. Due to the estimation mismatch and the passivity of Eavesdroppers (Eves), perfect channel state information (CSI) of wiretap links is challenging to obtain, which promotes our research on robust secure beamforming (BF) design in the IRS-assisted mmWave CRNs. This paper considers the collaborate scenario of Eves, which allows us to investigate the BF design in the harsh eavesdropping environment. Specifically, by using a uniform linear array (ULA) at the cognitive base station (CBS) and a uniform planar array (UPA) at the IRS, and supposing that imperfect CSIs of angle-of-departures for wiretap links are known, we formulate a constrained problem to maximize the worst-case achievable secrecy rate (ASR) of the secondary user (SU) by jointly designing the transmit BF at the CBS and reflect BF at the IRS. To solve the non-convex problem with coupled variables, an efficient alternating optimization algorithm is proposed. As for the transmit BF at the CBS, we propose a heuristic robust transmit BF algorithm to attain the BF vectors analytically. As for the reflect BF at the IRS, by means of an auxiliary variable, we transform the non-convex problem into a semi-definite programming problem with rank-1 constraint, which is handled with the help of an iterative penalty function, and then obtain the optimal reflect BF through CVX. Finally, simulation results indicate that the ASR performance of our proposed algorithm has a small gap with that of the optimal solution with perfect CSI compared with the other benchmarks.

Anonymous authentication has significant contribution to privacy protection in safety message dissemination for Vehicular Ad Hoc Networks (VANETs); however, it suffers from heavy workload of vehicle revocation check and message signature verification, which leads to unsatisfactory timeliness and high message loss rate in heavy traffic situations. To manage these challenging problems, this paper proposes an efficient safety message authentication protocol for VANETs by combining batch group signature verification and our proposed Group Session Key (GSK). Specifically, our signature verification method for safety message can both achieve computation efficiency by reducing the number of computation operations of bilinear pairing and resist impersonation attack by using tracking key implement. GSK is associated with the forward and backward keys and is only shared among unrevoked vehicle within a group. Performance analysis and simulations demonstrate that our protocol can provide promising security against various common types of attackers and is more efficient than traditional group-signature-based authentication protocols, in terms of computation time cost, authentication delay and message loss rate.