A New Efficient Method for the Detection of Intrusion in 5G and beyond Networks using ML

60-65The 5G networks are very important to support complex application by connecting different types of machines and devices, which provide the platform for different spoofing attacks. Traditional physical layer and cryptography authentication methods are facing problems in dynamic complex environment, including less reliability, security overhead also problem in predefined authentication system, giving protection and learn about time-varying attributes. In this paper, intrusion detection framework has been designed using various machine learning methods with the help of physical layer attributes and to provide more efficient system to increase the security. Machine learning methods for the intelligent intrusion detection are introduced, especially for supervised and non-supervised methods. Our machine learning based intelligent intrusion detection technique for the 5G and beyond networks is evaluated in terms of recall, precision, accuracy and f-value are validated for unpredictable dynamics and unknown conditions of networks

Higher Rate Secret Key Formation (HRKF) based on Physical Layer for Securing Vehicle-to-Vehicle Communication

One effort to secure vehicle-to-vehicle (V2V) communication is to use a symmetrical cryptographic scheme that requires the distribution of shared secret keys. To reduce attacks on key distribution, physical layer-based key formation schemes that utilize the characteristics of wireless channels have been implemented. However, existing schemes still produce a low bit formation rate (BFR) even though they can reach a low bit error rate (BER). Note that V2V communication requires a scheme with high BFR in order to fulfill its main goal of improving road safety. In this research, we propose a higher rate secret key formation (HRKF) scheme using received signal strength (RSS) as a source of random information. The focus of this research is to produce keys with high BFR without compromising BER. To reduce bit mismatch, we propose a polynomial regression method that can increase channel reciprocity. We also propose a fixed threshold quantization (FTQ) method to maintain the number of bits so that the BFR increases. The test results show that the HRKF scheme can increase BFR from 40% up to 100% compared to existing research schemes. To ensure the key cannot be guessed by the attacker, the HRKF scheme succeeds in producing a key that meets the randomness of the NIST test

Learning-aided physical layer authentication as an intelligent process

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machinelearning as an intelligent process to learn and utilize the complex time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single-dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments