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Various methods to predict stock prices have been studied. In the field of empirical finance, feature values for prediction include “value” and “momentum”. In this research, we use the pat...

Managing the flood of devices is a modern challenge in distributed networks as it is becoming increasingly difficult to manage the software updates needed to protect the devices from malicious attacks. Bitcoin, which is a blockchain-based system that relies on approximately 10,000 volunteer nodes to manage its ledger, faces similar problems. A centralized system mitigates this problem by enforcing update policies through one authority. In contrast, a distributed system with different update policies for different users could enter a worst-case scenario of sparse deployment due to forgotten updates or refusal to apply them. Nevertheless, we find that Bitcoin peer-software updates have been surprisingly successful in terms of evading this scenario. Therefore, we investigate the modeling of the impact caused by software version history and the remaining challenges, such as the inability to work well with update cycles. Consequently, we report that previous vulnerable software versions are still being used, and the share growth decelerates as the number of versions increases. Moreover, we clarify that the probability of recovering the adoption rate to previous levels is small, while surviving older versions increases the vulnerability of the network.

Blockchain is a trustless interoperability platform for users and is managed in a decentralized structure. There are no controllers in this platform, and service continuity depends on the users present in the network. Surveys of such network have mostly focused on miners who are authorized to write to the ledger; however, the non-mining nodes that maintain the ledger and are operated by volunteers with no direct incentive are also important indicators of stability. Although there are several projects that are inspired by or forked from Bitcoin, it is not intuitive to assume that projects not pertaining to Bitcoin are running in the same manner. Compared with major currencies, networks running minor currency nodes, despite having the same infrastructure, are more challenging to focus owing to their unclear operational realities. Thus, learning from the characteristics of failed projects is as important as learning from successful ones. Therefore, in this study, we investigate the unique features in the network by analyzing blockchain-based minor currencies. We analyze 317 minor cryptocurrency nodes and report that they contain locality, we also report on the issues of blacklisted nodes.

Blockchain is a service operated by a peer-to-peer type distributed network, and protocol control such as JSON-RPC is implemented as the interface for flexibility and operability. However, attacks that use protocol control against vulnerable and unmanaged interfaces have been reported. One of the methods to track cyber attacks on such a malicious user's network service is a honeypot that imitates the service and acquires attacker's behavior information. In this research, focusing on the Ethereum network, the behavior of malicious users is clarified using malicious communication history sent to simple honeypots installed in nine countries, Ethereum network information and darknet arrival packets. By analyzing these, the behavior of attackers and the tendency of requests were elucidated, and primary safety measures were established.

Blockchain is a service operated by a peer-to-peer type distributed network, and protocol control such as JSON-RPC is implemented as the interface for flexibility and operability. However, attacks that use protocol control against vulnerable and unmanaged interfaces have been reported. One of the methods to track cyber attacks on such a malicious user’s network service is a honeypot that imitates the service and acquires attacker’s behavior information. In this research, focusing on the Ethereum network, the behavior of malicious users is clarified using malicious communication history sent to simple honeypots installed in nine countries, Ethereum network information and darknet arrival packets. By analyzing these, the behavior of attackers and the tendency of requests were elucidated, and primary safety measures were established.

Various methods to predict stock prices have been studied. In the field of empirical finance, feature values for prediction include “value” and “momentum”. In this research, we use the pat...

Lysine-based amphipathic nonapeptides, including homochiral peptides [Ac-(l-Lys-l-Lys-Xaa)3-NH2 (Xaa=Gly, Ala, Aib, Ac5c, or Ac6c) and Ac-(d-Lys-d-Lys-Aib)3-NH2], a heterochiral peptide [Ac-(l-Lys-d-Lys-Aib)3-NH2], and a racemic mixture of diastereomeric peptides [Ac-(rac-Lys-rac-Lys-Aib)3-NH2] were designed and synthesized to investigate the relationship between their preferred secondary structures and their antimicrobial activity. Peptide 5, [Ac-(l-Lys-l-Lys-Ac6c)3-NH2] formed a stable α-helical structure and exhibited strong activity against Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa).

Bitcoin depends on Peer-to-Peer (P2P) network in a major way and shares the connecting IP address list with the nearest peer. In addition, the blockchain which is the basic technology can be accessed by anyone, and the transaction stored in the block can be checked anytime. Recent research has reported that anonymity of such a bitcoin P2P network is low, regardless of whether peer uses the anonymizers like TOR to keep the anonymity. This fact shows the risk of the malicious users being able to use this public information without exception. However, when the malicious user is hiding behind the network and browsing public information, it is difficult to distinguish between a malicious user and a honest one, and it is a challenge to detect signs of hidden threats. In this research, we propose a data mining approach to analyze by combining two kinds of IP address distributions: Bitcoion peer and malicious node (not in the bitcoin network), in order to obtain characteristics of hidden users. As a result, we confirmed that the nodes, which matched the first 24 bits of the IP address in the bitcoin network peer, sent the packet to the darknet. The contribution of this paper is three-fold: (1) we employ a novel approach to analyze a bitcoin network using Darknet dataset, (2) we identify the malicious node in the same network as the honest peer, and (3) we clarify the network deployments of Bitcoin peers and malicious nodes.

Various methods to predict stock prices have been studied. A typical method is based on time-series analysis; other methods are based on machine-learning techniques using cross-sectional data as feature values. In the field of empirical finance, feature values for prediction include “momentum”. The momentum strategy is simply based on past prices. Following the nearest trend, we buy current performers. From the different viewpoint from momentum, We’d like to challenge EMH. Our proposed method is following the similar trend. In other word, we look for past pattern similar to the current and predict from that. When predicting stock prices, investors sometimes refer to past markets that are similar to the current market. In this research, we propose a method to predict future stock prices with the past fluctuations similar to the current. As the levels of stock prices differ depending on the measured period, we develop a scaling method to compensate for the difference of price levels and the proposed new method; specifically, we propose indexing dynamic time warping (IDTW) to evaluate the similarities between time-series data. We apply the \(k^*\)-nearest neighbor algorithm with IDTW to predict stock prices for major stock indices and to assist users in making informed investment decisions. To demonstrate the advantages of the proposed method, we analyze its performance using major world indices. Experimental results show that the proposed method is more effective for predicting monthly stock price changes than other methods proposed by previous studies (Based on the comments received in Ai-Biz 2017, we clarified the differences from previous studies. And we added economic discussions about our proposed method such as differences from “momentum”, a challenge to Efficient Market Hypothesis and meanings as investment behavior).

In the field of portfolio management, practitioners are focusing increasingly on risk-based portfolios rather than on mean-variance portfolios. Risk-based portfolios are constructed based solely on covariance matrices, and include methods such as minimum variance (MV), risk parity (RP), and maximum diversification (MD). It is well known that the performance of a mean-variance portfolio depends on the accuracy of the estimations of the inputs. However, no studies have examined the relationship between the performance of risk-based portfolios and the estimated accuracy of covariance matrices. In this research, we compare the performance of risk-based portfolios for several estimation methods of covariance matrices in the Japanese stock market. In addition, we propose a highly accurate estimation method called cDCC-NLS, which incorporates nonlinear shrinkage into the cDCC-GARCH model. The results confirm that (1) the cDCC-NLS method shows the best estimation accuracy, (2) the RP and MD do not depend on the estimation accuracy of the covariance matrix, and (3) the MV does depend on the estimation accuracy of the covariance matrix.