The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network’s behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.

Since cyberattacks have become sophisticated in the form of advanced persistent threats (APTs), predicting and defending the APT attacks have drawn lots of attention. Although there have been related studies such as attack graphs, Hidden Markov Models, and Bayesian networks, they have four representative limitations; (i) non-standard attack modeling, (ii) lack of data-driven approaches, (iii) absence of real-world APT dataset, and (iv) high system dependability. In this paper, we propose Bayesian ATT&CK Network (BAN) which is based on system-independent data-driven approach. Specifically, BAN is based on Bayesian network, which adopts structure learning and parameter learning to model APT attackers with the MITRE ATT&CK® framework. The trained BAN aims to predict upcoming attack techniques and derives corresponding countermeasures. In addition, we prepare datasets via both automatic and manual labeling to overcome the data insufficiency issues of APT prediction. Experimental results show that BAN successfully contributes to handling APT attacks, given the best parameters extracted from extensive evaluations.

The goal of the "2019 Automatic Speaker Verification Spoofing and Countermeasures Challenge" (ASVspoof) was to make it easier to create systems that could identify voice spoofing attacks with high levels of accuracy. However, model complexity and latency requirements were not emphasized in the competition, despite the fact that they are stringent requirements for implementation in the real world. The majority of the top-performing solutions from the competition used an ensemble technique that merged numerous sophisticated deep learning models to maximize detection accuracy. Those approaches struggle with real-world deployment restrictions for voice assistants which would have restricted resources. We merged skip connection (from ResNet) and max feature map (from Light CNN) to create a compact system, and we tested its performance using the ASVspoof 2019 dataset. Our single model achieved a replay attack detection equal error rate (EER) of 0.30% on the evaluation set using an optimized constant Q transform (CQT) feature, outperforming the top ensemble system in the competition, which scored an EER of 0.39%. We experimented using depthwise separable convolutions (from MobileNet) to reduce model sizes; this resulted in an 84.3 percent reduction in parameter count (from 286K to 45K), while maintaining similar performance (EER of 0.36%). Additionally, we used Grad-CAM to clarify which spectrogram regions significantly contribute to the detection of fake data.

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.

This paper proposes a new clock offset estimation that mitigates unwanted link asymmetry for precise clock synchronization. The main contribution is to address the primary and traditional design issue of the IEEE 1588 standard precision time protocol (PTP), which estimates clock offset under the assumption that the delays of exchanged packets are symmetric. To mitigate the issue, we focus on the fact that PTP measures asymmetry variation through the derivatives of its timestamps with respect to the time step. By exploiting the measurement of the variation, the proposed approach defines the asymmetry in the form of a linear differential equation (LDE) and leverages the LDE to define and exclude asymmetry-induced errors. Additionally, we clearly derive the state transition of the asymmetry. Subsequently, we derive a novel state-space model from our approach. The model describes PTP clock offset estimation perfectly, allowing optimal clock offset estimation. We verify the theoretical validity of the proposed method with real data. Our approach improves PTP accuracy by more than thousand times and achieves an accuracy at the level of tens to hundreds of nanoseconds on an asymmetric communication link. Our approach realizes an accuracy comparable to that of PTPv2, without the cost of specialized hardware.

Electric Network Frequency (ENF) analysis has been an intriguing tool for multimedia forensics as former studies have paved the way for estimating ENF signals from digital audio, video, or even image files. However, for ENF signals to be widely used in extensive applications, supplementary research is needed so that ENF signals can be stably extracted without restrictions. In this letter, we propose a new phase-based approach for extracting ENF signals from CMOS sensor recordings. It uses phase differences between row signals from two consecutive frames, such that problems due to missing sample points during the idle periods are circumvented. The proposed method has substantial advantages in that it is applicable without a predefined read-out time and when the length of given videos is too short. Extensive experiments conducted with numerous devices demonstrate that the proposed method can take precedence over state-of-the-art methods because it robustly produces accurate ENF estimates in terms of alias frequency on the frame-level.