In recent years, drones have been widely used in many areas such as farming, movie making, surveillance and delivery. So, there is a need to protect these drones against security attacks including hijacking, spying and theft of stored data through the utilization of security mechanisms. Cryptographic keys are needed to operate these security mechanisms, and they must be generated by using random number generators which create unpredictable and non-regenerable random numbers. However, existing random number generators used in drones are not tailored for drones specifically as they use random sources generated on a desktop, not a drone. Recently, random number generators utilizing sensors in mobile phones and IoT (Internet of Things) devices have been studied, but are not appropriate for drones. In this paper, considering that drone sensors must be applied to flight and stationary modes, we proposed a drone specific random number generator called DroneRNG and implemented it. Then, we showed that our DroneRNG passed all of the NIST randomization tests and possesses better statistical properties and unpredictability than random number generators that are currently used in drones.

Anomaly detection is essential for many real-world applications, such as video surveillance, disease diagnosis, and visual inspection. With the development of neural networks, many neural networks have been used for anomaly detection by learning the distribution of normal data. However, they are vulnerable to distinguishing abnormalities when the normal and abnormal images are not significantly different. To mitigate this problem, we propose a novel loss function for one-class anomaly detection: decentralization loss. The main goal of the proposed method is to cause the latent feature of the encoder to disperse over the manifold space, such that the decoder can generate images similar to those in a normal class for any input. To this end, a decentralization term designed based on the dispersion measure for latent vectors is also added to the existing mean-squared error loss. To design a general solution for various datasets, we restrict the latent space by designing a decentralization loss term-based upper bound of the dispersion measure. As intended, a model trained with the proposed decentralization loss function disperses vectors on the manifold space and generates constant images. Consequently, the reconstruction error increases when the given test image is unknown. Experiments conducted on various datasets verify that the proposed function improves detection performance improved by about 1 % while reducing training time by 48 %, without any structural changes in the conventional autoencoder.

SUMMARYNetwork coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.

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The benefit of a smart manufacturing Industrial Internet of Things (IIoT) platform is that it can provide real-time monitoring, accurate analysis, and reporting for equipment by collecting data throughout the whole manufacturing facility. However, the increased internet connectivity of manufacturing machines or devices leads to various security vulnerabilities. In order to securely operate smart manufacturing IIoT systems in unmanned environments, it is necessary to establish a cryptographic key for protecting exchanged data between IIoT devices and stored data in the devices by using cryptographic algorithms. Especially, since the IIoT system is in an unmanned environment, the following two challenges must be solved: 1) The IIoT device must recover its own secret key without user interaction. 2) The IIoT device must prevent secret key recovery when anomaly situations such as unauthorized physical access occur. In this paper, we present a novel method to protect an IIoT device's secret key in unmanned smart manufacturing environments, called Two-Factor Device DNA-based Fuzzy Vault scheme. To satisfy the two challenges, our proposed method generates a specific two-factor device DNA through the combination of the IIoT device's intrinsic factor and its surrounding environments and then creates a vault set to conceal the secret key based on the two-factor device DNA. We also implement a prototype for ensuring the feasibility of our method by utilizing an EPUF and IEEE 802.15.4g receiver in a Raspberry Pi and a laptop, respectively, and then measure their performance. We then conduct experiments in an unmanned environment at the Smart Manufacturing Learning Center at Hanyang University by considering various normal and abnormal situations. Our experiment results show that the proposed method quickly extracts the secret key stored in the device in normal cases, but fails at key extraction in abnormal cases.