Unmanned Aerial Vehicles (UAVs) are aerial vehicles that can go to a particular position without human control or with remote human control. It is because unmanned control mainly relies on position estimation that a lot of research has been studied on GPS spoofing attacks. Detection methods against GPS spoofing attacks mainly include monitoring RF signals or IMU sensor values inside UAVs. In this work, we analyze GPS attack and detection in an advanced autopilot system, PX4 without excluding RF detection. Recent studies have shown that GPS spoofing is unable to evade the detection using EKF sensor fusion. Therefore, this paper experiment whether the detection could be evaded by strengthening the attacker model. This paper classifies attacker models according to whether an attacker knows the true position of UAVs or the estimated position by UAV and proposed attack methods depending on each model in PX4. We inject GPS value which our attack method intends to UAV during mission flight in simulation environment. By manipulating the GPS driver code of PX4 controller, we inject GPS value the attack method wants into UAV in physical environment. Finally, we observed the innovation test ratio during GPS spoofing and demonstrate that GPS spoofing attack is possible keeping the innovation test ratio under the anomaly detection criterion. After that, we proposed our approach which scales the EKF’s Kalman gain randomly to increase the detection method’s efficiency and experiment with the attack methods. This detection method has little effect on the test ratio without the attack. But during the attack, the innovation test ratio was increased higher than the anomaly detection criterion so that the attack could be detected.

Many automatic speech recognition (ASR) systems provide functions such as sending messages or opening garage doors. If such a system malfunctions, it could pose a significant threat. In this paper, we propose the attack scenario in which we transfer audio adversarial examples to radio system through the radio signal and the sound received through radio deceives the ASR system. In addition, we propose over-the-air technology using environmental noise so that we can attack easily and effectively. Using this method, we observed that the combined sound was much more robust to environmental noise than the non-combined sound. Finally, we experiment as our scenario on a real environment with adversarial examples created using environmental noise and show the results. Our attack succeeded against commercial ASR systems with a success rate of over 50%.

In recent years, researchers have studied various methods for transferring data in a network-separated environment, and the most representative method is the use of inaudible frequency signals like ultrasonic waves. This method has the advantage of being able to transfer data without other people noticing, but it has the disadvantage that speakers must exist. In a laboratory or company, external speakers may not be attached to each computer. Therefore, this paper presents a new covert channel attack that transfers data using internal speakers on the computer’s motherboard. The internal speaker can also produce a sound of the desired frequency, and, therefore, data can be transferred using high frequency sounds. We encode data into Morse code or binary code and transfer it. Then we record it using a smartphone. At this time, the location of the smartphone can be any distance within 1.5 m when the length per bit is longer than 50 ms, such as on the computer body or on the desk. Data are obtained by analyzing the recorded file. Our results show that data is transferred from a network-separated computer using an internal speaker with 20 bits/s in maximum.

Various attacks have occurred to extract information on a specific person from social networks. Differential privacy (DP) is one of the solutions for privacy disclosure issues. However, the privacy issue in social networks makes people reluctant to provide their data. This circumstance causes a lack of data for data analysis. DP in small data degrades data utility more than in big data when we add the same amount of noise. We propose Community Attributes Privacy-preserving Method (CAPM) using the sparse vector technique that maintains a constant privacy level even in small data to mitigate this issue in this paper. CAPM obfuscates raw graph data to protect the network structure in a small network. This technique can improve the data utility performance compared to the existing model. We also suggest a privacy parameter that sets the privacy budget based on the similarity of communities in a network to reflect the network topology and contribute to raising the accuracy of a synthetic graph. In a node privacy view, we inject noise into the edges of central nodes in a community. Finally, we evaluate CAPM with real networks regarding statistical utility and privacy protection. We show that CAPM has an error rate of the number of edges up to 20 percent and its structural entropy is less than 17 percent of the error rate on average. CAPM improves the average clustering coefficient by 82 percent from the recent modeling algorithm. In addition, a maximum 18 percent error rate in modularity outperforms the baseline whose 43 percent of error rate. The evaluation results show that the CAPM generates synthetic social graphs targeting their relations of communities and performs better in data utility.