Spoofing Attack Detection Using Machine Learning in Cross-Technology Communication

Cross-technology communication (CTC) technique can realize direct communication among heterogeneous wireless devices (e.g., WiFi, ZigBee, and Bluetooth in the 2.4 G ISM band) without gateway equipment for forwarding, which makes heterogeneous wireless communication more convenient and greatly reduces communication costs. However, compared with the traditional homogeneous network model, CTC technique also makes it easier to implement spoofing attacks in heterogeneous networks. WiFi devices with long communication distances and sufficient energy supply can directly launch spoofing attacks against ZigBee devices, which brings severe security concerns for heterogeneous wireless communications. In this paper, we focus on the CTC spoofing attack, especially spoofing attacks from WiFi to ZigBee and propose a machine learning-based method to detect spoofing attacks for heterogeneous wireless networks by using physical-layer information. First, we model the received signal strength (RSS) data of legitimate ZigBee devices to construct a one-class support vector machine (OSVM) classifier for detecting CTC spoofing attacks depending on the obtained training samples. Then, we simulated CTC spoofing attacks in a live testbed and evaluated the performance of our detection method. Results show that our approach is highly effective in spoofing detection. Even if the distance between the legitimate ZigBee device and WiFi attacker is near each other (i.e., less than 2 m) and does not require a large number of samples, the detection rate and precision of our method are both over 90%. Finally, we employ the OSVM classifier to obtain samples of spoofing attacks and then explore using SVM to further improve the performance of the classifier.

Secret Key Establishment Using Modified Quantization Log For Vehicular Ad-Hoc Network

Traditional cryptographic approaches such as symmetric and asymmetric cryptography are commonly employed to solve network security issues. The Secret Key Generation (SKG) system has the advantage of extracting secret keys from a wireless channel's physical layer information. It allows two wireless devices within the transmission range to extract a shared symmetric key without the use of a fixed key distribution infrastructure, allowing vehicular ad hoc networks to exchange information (VANET). This study aims to create a secure data communication system on the Vehicular Ad-Hoc Network using RSS Key Generation. Starting from the Modified Quantization Log, the results of the Modified Quantization Log show that the average KDRM between Alice and Bob is the average KDRM between Alice and Bob is 9.4%; meanwhile, the average KGR is 71.4 bps. This shows that the number of bit mismatches after the Modified Quantization Log process between the two valid users is already small, because they have used the pre-processing process in front of them, namely using the Kalman Filter and from the results of the BCH Code to be matched again so that it becomes the key. The next process is Universal Hash which is tested with the NIST test. The NIST Test parameters used are approximately entropy, frequency, block frequency, longest run, cumulative sum forward, and cumulative sum reverse. The existing results are appropriate; namely, the threshold in p whose value is above 0.01 is achieved. From the results of the Average Approximate Entropy, it is found that the largest value is obtained by the 40k10ms scheme, which is 0.7352.

Information Security Technology of Wireless Communication Physical Layer Based on Airspace Technology

Due to the characteristics of the broadcast function of the wireless communication network, the problem of information security has become very serious. And because the traditional upper-layer encryption technology is accompanied by the advancement of computer technology and eavesdropping technology, it has become more and more far from being able to meet the needs of modern society for information security. The multi-antenna beamforming technology in the airspace technology is a technology that can effectively increase the strength of the legal signal and suppress the eavesdropping signal. Therefore, it has become the most important technology in the physical layer security of our country and deserves our attention. Therefore, this article aims to study the wireless communication physical layer information security technology based on airspace technology. Based on the analysis of the basic theory of physical layer security, the current status of physical layer security research and the airspace physical layer security technology, the artificial noise auxiliary technology is promoted and applied to security identification. Taking the speed of maximizing the safe frequency spectrum as the design goal, an algorithm for joint optimization of information noise beam and artificial noise beam is proposed. The simulation results show that this algorithm can effectively improve the security and stability of the system, and it can also solve the eavesdropping problem.

Air Gesture Recognition Using WLAN Physical Layer Information

In recent years, the researchers have witnessed the important role of air gesture recognition in human-computer interactive (HCI), smart home, and virtual reality (VR). The traditional air gesture recognition method mainly depends on external equipment (such as special sensors and cameras) whose costs are high and also with a limited application scene. In this paper, we attempt to utilize channel state information (CSI) derived from a WLAN physical layer, a Wi-Fibased air gesture recognition system, namely, WiNum, which solves the problems of users’ privacy and energy consumption compared with the approaches using wearable sensors and depth cameras. In the process of recognizing the WiNum method, the collected raw data of CSI should be screened, among which can reflect the gesture motion. Meanwhile, the screened data should be preprocessed by noise reduction and linear transformation. After preprocessing, the joint of amplitude information and phase information is extracted, to match and recognize different air gestures by using the S-DTW algorithm which combines dynamic time warping algorithm (DTW) and support vector machine (SVM) properties. Comprehensive experiments demonstrate that under two different indoor scenes, WiNum can achieve higher recognition accuracy for air number gestures; the average recognition accuracy of each motion reached more than 93%, in order to achieve effective recognition of air gestures.