Detecting financial fraud to profile crimes and pinpoint system vulnerabilities is an essential issue in the financial industry. Because of interpretability requirements and the lack of mass transaction data due to privacy regulations, sophisticated handcrafted features have been adopted in much of the literature for fraud detection. In addition to established recency, frequency, monetary, and anomaly features, we propose behavior- and segmentation-type features based on statistical characteristics belonging solely to (non-)fraudulent accounts informed by financial expertise. Our proposed features are difficult for automatic feature generators to synthesize, and provide transparent cause-effect relationships and good prediction results. Features with time-inhomogeneous properties cause popular boosting classifiers such as XGBoost and LGBM to produce unstable detection results. We use the Kolmogorov–Smirnov test to detect and remove these features to improve XGBoost and LGBM detection performance and robustness. The resulting performance shown in our experiments is better than that of other classifiers, such as SVM and random forests. We examine the advantage of our technique by comparing it with several feature engineering works on fraud detection and automatic feature generation methods. On the other hand, we also find that generating training/testing sets with random sampling falsely eliminates such time inhomogeneity and results in misleading assessments of the robustness of machine learning models. These time-inhomogeneous phenomena also entail various modus operandi patterns, which influence the performance of different resampling methods for addressing data imbalance in fraud detection. Improper linear interpolation of SMOTE-related approaches leads to poor performance due to varying patterns of modi operandi. However, synthesizing fraudulent samples with simple oversampling and GANs mitigates this problem.

Software-defined networking (SDN) is a new networking architecture with a centralized control mechanism. SDN has proven to be successful in improving not only the network performance, but also security. However, centralized control in the SDN architecture is associated with new security vulnerabilities. In particular, user-datagram-protocol (UDP) flooding attacks can be easily launched and cause serious packet-transmission delays, controller-performance loss, and even network shutdown. In response to applications in the Internet of Things (IoT) field, this study considers UDP flooding attacks in SDN and proposes two lightweight countermeasures. The first method sometimes sacrifices address-resolution-protocol (ARP) requests to achieve a high level of security. In the second method, although packets must sometimes be sacrificed when undergoing an attack before starting to defend, the detection of the network state can prevent normal packets from being sacrificed. When blocking a network attack, attacks from the affected port are directly blocked without affecting normal ports. The performance and security of the proposed methods were confirmed by means of extensive experiments. Compared with the situation where no defense is implemented, or similar defense methods are implemented, after simulating a UDP flooding attack, our proposed method performed better in terms of the available bandwidth, central-processing-unit (CPU) consumption, and network delay time.

In health care, medication errors can result in serious health risks to patients, and hospitals require a secure medication administration system to prevent such errors. This paper therefore proposes a secure medication administration method based on threshold sharing technology. When a patient visits a doctor and the doctor prescribes n medications, a photo and the personal information of the patient are encoded into n QR code transparencies that can be decoded by common QR code scanners available for smartphones. The prescription and n QR code transparencies are then stored in a hospitals medication administration system. When the patient receives their medicine, they can scan these n QR code transparencies using a smartphone to ensure that they have all the medicines prescribed by the doctor; this function is accessible even if the patients phone does not have Internet access. The main purpose of the proposed system is to prevent hospitals from giving medicine to the wrong patient, or giving less than the prescribed dosage of medicine to the patient. The focus is not on the internal medicine packaging process in hospitals but on reducing the probability of counter staff giving medicine to the wrong patient. This function is of considerable importance to non-English-speaking people who are not used to reading medicine names in English.

Intelligent reflecting surface (IRS) composed of a large number of low‐cost, phase‐adjustable passive reflecting elements, which is an attractive solution of overcoming the signal attenuation and interference. IRS can be used as a low‐power passive system with signal enhancement and interference suppression. However, the planning problem of a large‐scale network contains a great numbers of base stations (BSs) and IRSs is challenging. The network planning problem considers limited deployment cost and signal enhancement at the same time is vital. Here, the 6G wireless signal coverage (WSC) problem with the consideration of both BS and IRS is considered. More specifically, the 6G WSC problem is first formulated through the integer linear programming. Due to its obvious NP‐hardness, two efficient heuristic algorithms that can reach a near‐optimal solution, that is, the WSC algorithm and the tree‐based WSC (TBWSC) algorithm is then proposed. Simulation‐based results showed that the proposed WSC algorithm achieves lower total cost than that of the TBWSC algorithm but also results in lower signal quality. The proposed TBWSC algorithm obtains the planning result with the highest signal quality and slightly higher total cost. Both proposed algorithms can find better planning results of multiple BSs and IRSs for 6G wireless communications.

A multi-purpose image-based QR code is designed in this paper. There are four purposes for the generated image-based QR code. In the first purpose, the basic image-based QR code with the look of a host image is with an ingenious layout to be identified easier. In the second one, a saliency region detection method is adopted for enhancing the quality of the image-based QR code. In the third one, the host image is embedded into the image-based QR code for further access to the host image; Finally, the visual cryptography-based watermarking method is applied to the host image embedded image-based QR code. In the case that the specific users need verification from the image-based QR code, the binary verified image can be retrieved when the public share is available. The experimental results demonstrate that the generated image-based QR code not only looked better than some previous works but also had high quality host image embedded and identification ability.

A pairs trading strategy (PTS) constructs a mean-reverting portfolio whose logarithmic value moves back and forth around a mean price level. It makes profits by longing (or shorting) the portfolio when it is underpriced (overpriced) and closing the portfolio when its value converges to the mean price level. The cointegration-based PTS literature uses the historical sample mean and variance to establish their open/close thresholds, which results in bias thresholds and less converged trades. We derive the asymptotic mean around which the portfolio value oscillates. Revised open/close thresholds determined by our asymptotic mean and standard derivations significantly improve PTS performance. The derivations of asymptotic means can be extended to construct a convergence rate filter mechanism to remove stock pairs that are unlikely to be profitable from trading to further reduce trading risks. Moreover, the PTS literature oversimplifies the joint problem of examining a stock pair’s cointegration property and selecting the fittest vector error correction model (VECM). We propose a two-step model selection procedure to determine the cointegration rank and the fittest VECM via the trace and likelihood ratio tests. We also derive an approximate simple integral trading volume ratio to meet no-odd-lot trading constraints. Experiments from Yuanta/P-shares Taiwan Top 50 Exchange Traded Fund and Yuanta/P-shares Taiwan Mid-Cap 100 Exchange Traded Fund constituent stock tick-by-tick backtesting during 2015–2018 show remarkable improvements by adopting our approaches.